Multi-partitioning for combination operations

ABSTRACT

Systems and methods are disclosed for processing and executing queries against one or more dataset. As part of processing the query, the system determines whether the query is susceptible to a significantly imbalanced partition. In the event, the query is susceptible to an imbalanced partition, the system monitors the query and determines whether to perform a multi-partitioning determination to avoid a significantly imbalanced partition.

RELATED APPLICATIONS

Any application referenced herein is hereby incorporated by reference inits entirety. Any and all applications for which a foreign or domesticpriority claim is identified in the Application Data Sheet as filed withthe present application are incorporated by reference under 37 CFR 1.57and made a part of this specification. This application is acontinuation of U.S. patent application Ser. No. 15/714,029, filed onSep. 25, 2017, entitled MULTI-PARTITIONING DETERMINATION FOR COMBINATIONOPERATIONS, which is incorporated herein by reference in its entirety.

FIELD

At least one embodiment of the present disclosure pertains to one ormore tools for facilitating searching and analyzing large sets of datato locate data of interest.

BACKGROUND

Information technology (IT) environments can include diverse types ofdata systems that store large amounts of diverse data types generated bynumerous devices. For example, a big data ecosystem may includedatabases such as MySQL and Oracle databases, cloud computing servicessuch as Amazon web services (AWS), and other data systems that storepassively or actively generated data, including machine-generated data(“machine data”). The machine data can include performance data,diagnostic data, or any other data that can be analyzed to diagnoseequipment performance problems, monitor user interactions, and to deriveother insights.

The large amount and diversity of data systems containing large amountsof structured, semi-structured, and unstructured data relevant to anysearch query can be massive, and continues to grow rapidly. Thistechnological evolution can give rise to various challenges in relationto managing, understanding and effectively utilizing the data. To reducethe potentially vast amount of data that may be generated, some datasystems pre-process data based on anticipated data analysis needs. Inparticular, specified data items may be extracted from the generateddata and stored in a data system to facilitate efficient retrieval andanalysis of those data items at a later time. At least some of theremainder of the generated data is typically discarded duringpre-processing.

However, storing massive quantities of minimally processed orunprocessed data (collectively and individually referred to as “rawdata”) for later retrieval and analysis is becoming increasingly morefeasible as storage capacity becomes more inexpensive and plentiful. Ingeneral, storing raw data and performing analysis on that data later canprovide greater flexibility because it enables an analyst to analyze allof the generated data instead of only a fraction of it.

Although the availability of vastly greater amounts of diverse data ondiverse data systems provides opportunities to derive new insights, italso gives rise to technical challenges to search and analyze the data.For example, in some cases, a user may want to use multiple partitionsand distributed processing cores to combine two or more large datasetsin some fashion, such as by using a join operation. However, in certaincases, combining the datasets can result in one or more significantlyimbalanced partitions and a single processing core being tasked withprocessing a disproportionately large number of data entries as comparedto other processor cores. This imbalance can result in a significantdelay of the entire set of results until the processor core finishesprocessing the imbalanced partition.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and notlimitation, in the figures of the accompanying drawings, in which likereference numerals indicate similar elements and in which:

FIG. 1A is a block diagram of an example environment in which anembodiment may be implemented;

FIG. 1B is a block diagram of an example networked computer environment,in accordance with example embodiments;

FIG. 2 is a block diagram of an example data intake and query system, inaccordance with example embodiments;

FIG. 3 is a block diagram of an example cloud-based data intake andquery system, in accordance with example embodiments;

FIG. 4 is a block diagram of an example data intake and query systemthat performs searches across external data systems, in accordance withexample embodiments;

FIG. 5A is a flowchart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments;

FIG. 5B is a block diagram of a data structure in which time-stampedevent data can be stored in a data store, in accordance with exampleembodiments;

FIG. 5C provides a visual representation of the manner in which apipelined search language or query operates, in accordance with exampleembodiments;

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments;

FIG. 6B provides a visual representation of an example manner in which apipelined command language or query operates, in accordance with exampleembodiments;

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments;

FIG. 7B illustrates an example of processing keyword searches and fieldsearches, in accordance with disclosed embodiments;

FIG. 7C illustrates an example of creating and using an inverted index,in accordance with example embodiments;

FIG. 7D depicts a flowchart of example use of an inverted index in apipelined search query, in accordance with example embodiments;

FIG. 8A is an interface diagram of an example user interface for asearch screen, in accordance with example embodiments;

FIG. 8B is an interface diagram of an example user interface for a datasummary dialog that enables a user to select various data sources, inaccordance with example embodiments;

FIG. 9 is an example search query received from a client and executed bysearch peers, in accordance with example embodiments;

FIG. 10 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources;

FIG. 11 is a block diagram illustrating an embodiment of multiplemachines, each having multiple nodes;

FIG. 12 is a diagram illustrating an embodiment of a DAG;

FIG. 13 is a block diagram illustrating an embodiment of partitionsimplementing various search phases of a DAG;

FIG. 14 is a data flow diagram illustrating an embodiment ofcommunications between various components within the environment toprocess and execute a query;

FIG. 15 is a flow diagram illustrative of an embodiment of a routine toprovide query results;

FIG. 16 is a flow diagram illustrative of an embodiment of a routine toprocess a query;

FIG. 17 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources including common storage;

FIG. 18 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources including an ingested data buffer;

FIG. 19 is a flow diagram illustrative of an embodiment of a routine toprocess and execute a query;

FIG. 20 is a flow diagram illustrative of an embodiment of amulti-partition routine;

FIG. 21 is a diagram illustrating an embodiment of a join operationperformed on two datasets;

FIG. 22 is a flow diagram illustrative of an embodiment of amulti-partition routine;

FIG. 23 is a diagram illustrating an embodiment of a join operationperformed on two datasets;

FIG. 24 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

1.0. GENERAL OVERVIEW

2.0. OVERVIEW OF DATA INTAKE AND QUERY SYSTEMS

3.0. GENERAL OVERVIEW

-   -   3.1 HOST DEVICES    -   3.2 CLIENT DEVICES    -   3.3. CLIENT DEVICE APPLICATIONS    -   3.4. DATA SERVER SYSTEM    -   3.5. CLOUD-BASED SYSTEM OVERVIEW    -   3.6. SEARCHING EXTERNALLY-ARCHIVED DATA    -   3.7. DATA INGESTION        -   3.7.1. INPUT        -   3.7.2. PARSING        -   3.7.3. INDEXING    -   3.8. QUERY PROCESSING    -   3.9. PIPELINED SEARCH LANGUAGE    -   3.10. FIELD EXTRACTION    -   3.11. EXAMPLE SEARCH SCREEN    -   3.12. DATA MODELS    -   3.13. ACCELERATION TECHNIQUE        -   3.13.1. AGGREGATION TECHNIQUE        -   3.13.2. KEYWORD INDEX        -   3.13.3. HIGH PERFORMANCE ANALYTICS STORE        -   3.13.4. EXTRACTING EVENT DATA USING POSTING        -   3.13.5. ACCELERATING REPORT GENERATION    -   3.14. SECURITY FEATURES

4.0. DATA INTAKE AND FABRIC SYSTEM ARCHITECTURE

-   -   4.1. WORKER NODES        -   4.1.1. SERIALIZATION/DESERIALIZATION    -   4.2. SEARCH PROCESS MASTER        -   4.2.1 WORKLOAD CATALOG        -   4.2.2 NODE MONITOR        -   4.2.3 DATASET COMPENSATION    -   4.3. QUERY COORDINATOR        -   4.3.1. QUERY PROCESSING        -   4.3.2. QUERY EXECUTION AND NODE CONTROL        -   4.3.3. RESULT PROCESSING    -   4.4 QUERY ACCELERATION DATA STORE

5.0. QUERY DATA FLOW

6.0. QUERY COORDINATOR FLOW

7.0. QUERY PROCESSING FLOW

8.0. COMMON STORAGE ARCHITECTURE

9.0. INGESTED DATA BUFFER ARCHITECTURE

10.0 COMBINING DATASETS

-   -   10.1 MULTI-PARTITION DETERMINATION    -   10.2 MULTI-PARTITION OPERATION

11.0. HARDWARE EMBODIMENT

12.0. TERMINOLOGY

In this description, references to “an embodiment,” “one embodiment,” orthe like, mean that the particular feature, function, structure orcharacteristic being described is included in at least one embodiment ofthe technique introduced herein. Occurrences of such phrases in thisspecification do not necessarily all refer to the same embodiment. Onthe other hand, the embodiments referred to are also not necessarilymutually exclusive.

A data intake and query system can index and store data in data storesof indexers, and can receive search queries causing a search of theindexers to obtain search results. The data intake and query systemtypically has search, extraction, execution, and analytics capabilitiesthat may be limited in scope to the data stores of the indexers(“internal data stores”). Hence, a seamless and comprehensive search andanalysis that includes diverse data types from external data sources,common storage (may also be referred to as global data storage or globaldata stores), ingested data buffers, query acceleration data stores,etc. may be difficult. Thus, the capabilities of some data intake andquery systems remain isolated from a variety of data sources that couldimprove search results to provide new insights. Furthermore, theprocessing flow of some data intake and query systems are unidirectionalin that data is obtained from a data source, processed, and thencommunicated to a search head or client without the ability to routedata to different destinations.

The disclosed embodiments overcome these drawbacks by extending thesearch and analytics capabilities of a data intake and query system toinclude diverse data types stored in diverse data systems internal to orexternal from the data intake and query system. As a result, an analystcan use the data intake and query system to search and analyze data froma wide variety of dataset sources, including enterprise systems and opensource technologies of a big data ecosystem. The term “big data” refersto large data sets that may be analyzed computationally to revealpatterns, trends, and associations, in some cases, relating to humanbehavior and interactions.

In particular, introduced herein is a data intake and query system thatthat has the ability to execute big data analytics seamlessly and canscale across diverse data sources to enable processing large volumes ofdiverse data from diverse data systems. A “data source” can include a“data system,” which may refer to a system that can process and/or storedata. A “data storage system” may refer to a storage system that canstore data such as unstructured, semi-structured, or structured data.Accordingly, a data source can include a data system that includes adata storage system.

The system can improve search and analytics capabilities of previoussystems by employing a search process master and query coordinatorscombined with a scalable network of distributed nodes communicativelycoupled to diverse data systems. The network of distributed nodes canact as agents of the data intake and query system to collect and processdata of distributed data systems, and the search process master andcoordinators can provide the processed data to the search head as searchresults.

For example, the data intake and query system can respond to a query byexecuting search operations on various internal and external datasources to obtain partial search results that are harmonized andpresented as search results of the query. As such, the data intake andquery system can offload search and analytics operations to thedistributed nodes. Hence, the system enables search and analyticscapabilities that can extend beyond the data stored on indexers toinclude external data systems, common storage, query acceleration datastores, ingested data buffers, etc.

The system can provide big data open stack integration to act as a bigdata pipeline that extends the search and analytics capabilities of asystem over numerous and diverse data sources. For example, the systemcan extend the data execution scope of the data intake and query systemto include data residing in external data systems such as MySQL,PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra,Mongo DB; cloud storage like Amazon S3 and Hadoop distributed filesystem (HDFS); common storage; ingested data buffers; etc. Thus, thesystem can execute search and analytics operations for all possiblecombinations of data types stored in various data sources.

The distributed processing of the system enables scalability to includeany number of distributed data systems. As such, queries received by thedata intake and query system can be propagated to the network ofdistributed nodes to extend the search and analytics capabilities of thedata intake and query system over different data sources. In thiscontext, the network of distributed nodes can act as an extension of thelocal data intake in query system's data processing pipeline tofacilitate scalable analytics across the diverse data systems.Accordingly, the system can extend and transform the data intake andquery system to include data resources into a data fabric platform thatcan leverage computing assets from anywhere and access and execute ondata regardless of type or origin.

The disclosed embodiments include services such as new searchcapabilities, visualization tools, and other services that areseamlessly integrated into the DFS system. For example, the disclosedtechniques include new search services performed on internal datastores, external data stores, or a combination of both. The searchoperations can provide ordered or unordered search results, or searchresults derived from data of diverse data systems, which can bevisualized to provide new and useful insights about the data containedin a big data ecosystem.

Various other features of the DFS system introduced here will becomeapparent from the description that follows. First, however, it is usefulto consider an example of an environment and system in which thetechniques can be employed, as will now be described.

1.0. General Overview

The embodiments disclosed herein generally refer to an environment thatincludes data intake and query system including a data fabric servicesystem architecture (“DFS system”), services, a network of distributednodes, and distributed data systems, all interconnected over one or morenetworks. However, embodiments of the disclosed environment can includemany computing components including software, servers, routers, clientdevices, and host devices that are not specifically described herein. Asused herein, a “node” can refer to one or more devices and/or softwarerunning on devices that enable the devices to provide execute a task ofthe system. For example, a node can include devices running softwarethat enable the device to execute a portion of a query.

FIG. 1A is a high-level system diagram of an environment 10 in which anembodiment may be implemented. The environment 10 includes distributedexternal data systems 12-1 and 12-2 (also referred to collectively andindividually as external data system(s) 12). The external data systems12 are communicatively coupled (e.g., via a LAN, WAN, etc.) to workernodes 14-1 and 14-2 of a data intake and query system 16, respectively(also referred to collectively and individually as worker node(s) 14).The environment 10 can also include a client device 22 and applicationsrunning on the client device 22. An example includes a personalcomputer, laptop, tablet, phone, or other computing device running anetwork browser application that enables a user of the client device 22to access any of the data systems.

The data intake and query system 16 and the external data systems 12 caneach store data obtained from various data sources. For example, thedata intake and query system 16 can store data in internal data stores20 (also referred to as an internal storage system), and the externaldata systems 12 can store data in respective external data stores 22(also referred to as external storage systems). However, the data intakeand query system 16 and external data systems 12 may process and storedata differently. For example, as explained in greater detail below, thedata intake and query system 16 may store minimally processed orunprocessed data (“raw data”) in the internal data stores 20, which canbe implemented as local data stores 20-1, common storage 20-2, or queryacceleration data stores 20-3. In contrast, the external data systems 12may store pre-processed data rather than raw data. Hence, the dataintake and query system 16 and the external data systems 12 can operateindependent of each other in a big data ecosystem.

The worker nodes 14 can act as agents of the data intake and querysystem 16 to process data collected from the internal data stores 20 andthe external data stores 22. The worker nodes 14 may reside on one ormore computing devices such as servers communicatively coupled to theexternal data systems 12. Other components of the data intake and querysystem 16 can finalize the results before returning the results to theclient device 22. As such, the worker nodes 14 can extend the search andanalytics capabilities of the data intake and query system 16 to act ondiverse data systems.

The external data systems 12 may include one or more computing devicesthat can store structured, semi-structured, or unstructured data. Eachexternal data system 12 can generate and/or collect generated data, andstore the generated data in their respective external data stores 22.For example, the external data system 12-1 may include a server runninga MySQL database that stores structured data objects such astime-stamped events, and the external data system 12-2 may be a serverof cloud computing services such as Amazon web services (AWS) that canprovide different data types ranging from unstructured (e.g., s3) tostructured (e.g., redshift).

The internal data stores 20 are said to be internal because the datastored thereon has been processed or passed through the data intake andquery system 16 in some form. Conversely, the external data systems 12are said to be external to the data intake and query system 16 becausethe data stored at the external data stores 22 has not necessarily beenprocessed or passed through the data intake and query system 16. Inother words, the data intake and query system 16 may have no control orinfluence over how data is processed, controlled, or managed by theexternal data systems 12.

The external data systems 12 can process data, perform requests receivedfrom other computing systems, and perform numerous other computationaltasks independent of each other and independent of the data intake andquery system 16. For example, the external data system 12-1 may be aserver that can process data locally that reflects correlations amongthe stored data. The external data systems 12 may generate and/or storeever increasing volumes of data without any interaction with the dataintake and query system 16. As such, each of the external data system 12may act independently to control, manage, and process the data theycontain.

Data stored in the internal data stores 20 and external data stores 22may be related. For example, an online transaction could generatevarious forms of data stored in disparate locations and in variousformats. The generated data may include payment information, customerinformation, and information about suppliers, retailers, and the like.Other examples of data generated in a big data ecosystem includeapplication program data, system logs, network packet data, error logs,stack traces, and performance data. The data can also include diagnosticinformation and many other types of data that can be analyzed to performlocal actions, diagnose performance problems, monitor interactions, andderive other insights.

The volume of generated data can grow at very high rates as the numberof transactions and diverse data systems grows. A portion of this largevolume of data could be processed and stored by the data intake andquery system 16 while other portions could be stored in any of theexternal data systems 12. In an effort to reduce the vast amounts of rawdata generated in a big data ecosystem, some of the external datasystems 12 may pre-process the raw data based on anticipated dataanalysis needs, store the pre-processed data, discard some or all of theremaining raw data, or store it in a different location that data intakeand query system 16 does not have access to. However, discarding or notmaking the massive amounts of raw data available can result in the lossof valuable insights that could have been obtained by searching all ofthe raw data.

In contrast, the data intake and query system 16 can address some ofthese challenges by collecting and storing raw data as structured“events,” as will be described in greater detail below. In someembodiments, an event includes a portion of raw data and is associatedwith a specific point in time. For example, events may be derived from“time series data,” where the time series data comprises a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time.

In some embodiments, the external data systems 12 can store raw data asevents that are indexed by timestamps but are also associated withpredetermined data items. This structure is essentially a modificationof conventional database systems that require predetermining data itemsfor subsequent searches. These systems can be modified to retain theremaining raw data for subsequent re-processing for other predetermineddata items.

Specifically, the raw data can be divided into segments and indexed bytimestamps. The predetermined data items can be associated with theevents indexed by timestamps. The events can be searched only for thepredetermined data items during search time; the events can bere-processed later in time to re-index the raw data, and generate eventswith new predetermined data items. As such, the data systems of thesystem 10 can store related data in a variety of pre-processed data andraw data in a variety of structures.

A number of tools are available to search and analyze data contained inthese diverse data systems. As such, an analyst can use a tool to searcha database of the external data system 12-1. A different tool could beused to search a cloud services application of the external data system12-2. Yet another different tool could be used to search the internaldata stores 20. Moreover, different tools can perform analytics of datastored in proprietary or open source data stores. However, existingtools cannot obtain valuable insights from data contained in acombination of the data intake and query system 16 and/or any of theexternal data systems 12. Examples of these valuable insights mayinclude correlations between the structured data of the external datastores 22 and raw data of the internal data stores 20.

The disclosed techniques can extend the search, extraction, execution,and analytics capabilities of data intake and query systems toseamlessly search and analyze multiple diverse data of diverse datasystems in a big data ecosystem. The disclosed techniques can transforma big data ecosystem into a big data pipeline between external datasystems and a data intake and query system, to enable seamless searchand analytics operations on a variety of data sources, which can lead tonew insights that were not previously available. Hence, the disclosedtechniques include a data intake and query system 16 extended to searchexternal data systems into a data fabric platform that can leveragecomputing assets from anywhere and access and execute on data regardlessof type and origin. In addition, the data intake and query system 16facilitates implementation of both iterative searches, to read datasetsmultiple times in a loop, and interactive or exploratory data analysis(e.g., for repeated database-style querying of data).

2.0. Overview of Data Intake and Query Systems

As indicated above, modern data centers and other computing environmentscan comprise anywhere from a few host computer systems to thousands ofsystems configured to process data, service requests from remoteclients, and perform numerous other computational tasks. Duringoperation, various components within these computing environments oftengenerate significant volumes of machine data. Machine data is any dataproduced by a machine or component in an information technology (IT)environment and that reflects activity in the IT environment. Forexample, machine data can be raw machine data that is generated byvarious components in IT environments, such as servers, sensors,routers, mobile devices, Internet of Things (IoT) devices, etc. Machinedata can include system logs, network packet data, sensor data,application program data, error logs, stack traces, system performancedata, etc. In general, machine data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data. In order toreduce the size of the potentially vast amount of machine data that maybe generated, many of these tools typically pre-process the data basedon anticipated data-analysis needs. For example, pre-specified dataitems may be extracted from the machine data and stored in a database tofacilitate efficient retrieval and analysis of those data items atsearch time. However, the rest of the machine data typically is notsaved and is discarded during pre-processing. As storage capacitybecomes progressively cheaper and more plentiful, there are fewerincentives to discard these portions of machine data and many reasons toretain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and search machine datafrom various websites, applications, servers, networks, and mobiledevices that power their businesses. The data intake and query system isparticularly useful for analyzing data which is commonly found in systemlog files, network data, and other data input sources. Although many ofthe techniques described herein are explained with reference to a dataintake and query system similar to the SPLUNK® ENTERPRISE system, thesetechniques are also applicable to other types of data systems.

In the data intake and query system, machine data are collected andstored as “events”. An event comprises a portion of machine data and isassociated with a specific point in time. The portion of machine datamay reflect activity in an IT environment and may be produced by acomponent of that IT environment, where the events may be searched toprovide insight into the IT environment, thereby improving theperformance of components in the IT environment. Events may be derivedfrom “time series data,” where the time series data comprises a sequenceof data points (e.g., performance measurements from a computer system,etc.) that are associated with successive points in time. In general,each event has a portion of machine data that is associated with atimestamp that is derived from the portion of machine data in the event.A timestamp of an event may be determined through interpolation betweentemporally proximate events having known timestamps or may be determinedbased on other configurable rules for associating timestamps withevents.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data associated withfields in a database table. In other instances, machine data may nothave a predefined format (e.g., may not be at fixed, predefinedlocations), but may have repeatable (e.g., non-random) patterns. Thismeans that some machine data can comprise various data items ofdifferent data types that may be stored at different locations withinthe data. For example, when the data source is an operating system log,an event can include one or more lines from the operating system logcontaining machine data that includes different types of performance anddiagnostic information associated with a specific point in time (e.g., atimestamp).

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The machine data generated bysuch data sources can include, for example and without limitation,server log files, activity log files, configuration files, messages,network packet data, performance measurements, sensor measurements, etc.

The data intake and query system uses a flexible schema to specify howto extract information from events. A flexible schema may be developedand redefined as needed. Note that a flexible schema may be applied toevents “on the fly,” when it is needed (e.g., at search time, indextime, ingestion time, etc.). When the schema is not applied to eventsuntil search time, the schema may be referred to as a “late-bindingschema.”

During operation, the data intake and query system receives machine datafrom any type and number of sources (e.g., one or more system logs,streams of network packet data, sensor data, application program data,error logs, stack traces, system performance data, etc.). The systemparses the machine data to produce events each having a portion ofmachine data associated with a timestamp. The system stores the eventsin a data store. The system enables users to run queries against thestored events to, for example, retrieve events that meet criteriaspecified in a query, such as criteria indicating certain keywords orhaving specific values in defined fields. As used herein, the term“field” refers to a location in the machine data of an event containingone or more values for a specific data item. A field may be referencedby a field name associated with the field. As will be described in moredetail herein, a field is defined by an extraction rule (e.g., a regularexpression) that derives one or more values or a sub-portion of textfrom the portion of machine data in each event to produce a value forthe field for that event. The set of values produced aresemantically-related (such as IP address), even though the machine datain each event may be in different formats (e.g., semantically-relatedvalues may be in different positions in the events derived fromdifferent sources).

As described above, the system stores the events in a data store. Theevents stored in the data store are field-searchable, wherefield-searchable herein refers to the ability to search the machine data(e.g., the raw machine data) of an event based on a field specified insearch criteria. For example, a search having criteria that specifies afield name “UserID” may cause the system to field-search the machinedata of events to identify events that have the field name “UserID.” Inanother example, a search having criteria that specifies a field name“UserID” with a corresponding field value “12345” may cause the systemto field-search the machine data of events to identify events havingthat field-value pair (e.g., field name “UserID” with a correspondingfield value of “12345”). Events are field-searchable using one or moreconfiguration files associated with the events. Each configuration fileincludes one or more field names, where each field name is associatedwith a corresponding extraction rule and a set of events to which thatextraction rule applies. The set of events to which an extraction ruleapplies may be identified by metadata associated with the set of events.For example, an extraction rule may apply to a set of events that areeach associated with a particular host, source, or source type. Whenevents are to be searched based on a particular field name specified ina search, the system uses one or more configuration files to determinewhether there is an extraction rule for that particular field name thatapplies to each event that falls within the criteria of the search. Ifso, the event is considered as part of the search results (andadditional processing may be performed on that event based on criteriaspecified in the search). If not, the next event is similarly analyzed,and so on.

As noted above, the data intake and query system utilizes a late-bindingschema while performing queries on events. One aspect of a late-bindingschema is applying extraction rules to events to extract values forspecific fields during search time. More specifically, the extractionrule for a field can include one or more instructions that specify howto extract a value for the field from an event. An extraction rule cangenerally include any type of instruction for extracting values fromevents. In some cases, an extraction rule comprises a regularexpression, where a sequence of characters form a search pattern. Anextraction rule comprising a regular expression is referred to herein asa regex rule. The system applies a regex rule to an event to extractvalues for a field associated with the regex rule, where the values areextracted by searching the event for the sequence of characters definedin the regex rule.

In the data intake and query system, a field extractor may be configuredto automatically generate extraction rules for certain fields in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields specified in aquery may be provided in the query itself, or may be located duringexecution of the query. Hence, as a user learns more about the data inthe events, the user can continue to refine the late-binding schema byadding new fields, deleting fields, or modifying the field extractionrules for use the next time the schema is used by the system. Becausethe data intake and query system maintains the underlying machine dataand uses a late-binding schema for searching the machine data, itenables a user to continue investigating and learn valuable insightsabout the machine data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent and/or similar data items, even thoughthe fields may be associated with different types of events thatpossibly have different data formats and different extraction rules. Byenabling a common field name to be used to identify equivalent and/orsimilar fields from different types of events generated by disparatedata sources, the system facilitates use of a “common information model”(CIM) across the disparate data sources (further discussed with respectto FIG. 7A).

3.0. General Overview

FIG. 1B is a block diagram of an example networked computer environment100, in accordance with example embodiments. Those skilled in the artwould understand that FIG. 1B represents one example of a networkedcomputer system and other embodiments, such as the embodimentillustrated in FIG. 1A may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In some embodiments, one or more client devices 102 are coupled to oneor more host devices 106 and a data intake and query system 108 via oneor more networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

3.1 Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types of machine data.For example, a host application 114 comprising a web server may generateone or more web server logs in which details of interactions between theweb server and any number of client devices 102 is recorded. As anotherexample, a host device 106 comprising a router may generate one or morerouter logs that record information related to network traffic managedby the router. As yet another example, a host application 114 comprisinga database server may generate one or more logs that record informationrelated to requests sent from other host applications 114 (e.g., webservers or application servers) for data managed by the database server.

3.2 Client Devices

Client devices 102 represent any computing device capable of interactingwith one or more host devices 106 via a network 104. Examples of clientdevices 102 may include, without limitation, smart phones, tabletcomputers, handheld computers, wearable devices, laptop computers,desktop computers, servers, portable media players, gaming devices, andso forth. In general, a client device 102 can provide access todifferent content, for instance, content provided by one or more hostdevices 106, etc. Each client device 102 may comprise one or more clientapplications 110, described in more detail in a separate sectionhereinafter.

3.3. Client Device Applications

In some embodiments, each client device 102 may host or execute one ormore client applications 110 that are capable of interacting with one ormore host devices 106 via one or more networks 104. For instance, aclient application 110 may be or comprise a web browser that a user mayuse to navigate to one or more websites or other resources provided byone or more host devices 106. As another example, a client application110 may comprise a mobile application or “app.” For example, an operatorof a network-based service hosted by one or more host devices 106 maymake available one or more mobile apps that enable users of clientdevices 102 to access various resources of the network-based service. Asyet another example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In some embodiments, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In some embodiments, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some embodiments, an SDK or other code for implementing themonitoring functionality may be offered by a provider of a data intakeand query system, such as a system 108. In such cases, the provider ofthe system 108 can implement the custom code so that performance datagenerated by the monitoring functionality is sent to the system 108 tofacilitate analysis of the performance data by a developer of the clientapplication or other users.

In some embodiments, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In some embodiments, the monitoring component 112 may monitor one ormore aspects of network traffic sent and/or received by a clientapplication 110. For example, the monitoring component 112 may beconfigured to monitor data packets transmitted to and/or from one ormore host applications 114. Incoming and/or outgoing data packets can beread or examined to identify network data contained within the packets,for example, and other aspects of data packets can be analyzed todetermine a number of network performance statistics. Monitoring networktraffic may enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In some embodiments, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In some embodiments, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In some embodiments, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer, and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

3.4. Data Server System

FIG. 2 is a block diagram of an example data intake and query system108, in accordance with example embodiments. System 108 includes one ormore forwarders 204 that receive data from a variety of input datasources 202, and one or more indexers 206 that process and store thedata in one or more data stores 208. These forwarders 204 and indexers206 can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by system 108. Examples of a data sources 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In some embodiments, a forwarder 204 may comprise a service accessibleto client devices 102 and host devices 106 via a network 104. Forexample, one type of forwarder 204 may be capable of consuming vastamounts of real-time data from a potentially large number of clientdevices 102 and/or host devices 106. The forwarder 204 may, for example,comprise a computing device which implements multiple data pipelines or“queues” to handle forwarding of network data to indexers 206. Aforwarder 204 may also perform many of the functions that are performedby an indexer. For example, a forwarder 204 may perform keywordextractions on raw data or parse raw data to create events. A forwarder204 may generate time stamps for events. Additionally or alternatively,a forwarder 204 may perform routing of events to indexers 206. Datastore 208 may contain events derived from machine data from a variety ofsources all pertaining to the same component in an IT environment, andthis data may be produced by the machine in question or by othercomponents in the IT environment.

3.5. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 3 illustrates a block diagram of an example cloud-based data intakeand query system. Similar to the system of FIG. 2, the networkedcomputer system 300 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system300, one or more forwarders 204 and client devices 302 are coupled to acloud-based data intake and query system 306 via one or more networks304. Network 304 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 302 and forwarders204 to access the system 306. Similar to the system of 38, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 306 forfurther processing.

In some embodiments, a cloud-based data intake and query system 306 maycomprise a plurality of system instances 308. In general, each systeminstance 308 may include one or more computing resources managed by aprovider of the cloud-based system 306 made available to a particularsubscriber. The computing resources comprising a system instance 308may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 302 to access a web portal or otherinterface that enables the subscriber to configure an instance 308.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers, andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 308) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment, such as SPLUNK® ENTERPRISE, and acloud-based environment, such as SPLUNK CLOUD™, are centrally visible).

3.6. Searching Externally-Archived Data

FIG. 4 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the Splunk® Analytics for Hadoop® system provided bySplunk Inc. of San Francisco, Calif. Splunk® Analytics for Hadoop®represents an analytics platform that enables business and IT teams torapidly explore, analyze, and visualize data in Hadoop® and NoSQL datastores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 404 over network connections420. As discussed above, the data intake and query system 108 may residein an enterprise location, in the cloud, etc. FIG. 4 illustrates thatmultiple client devices 404 a, 404 b, . . . , 404 n may communicate withthe data intake and query system 108. The client devices 404 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 4 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a software developerkit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 404 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 410. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 410, 412. FIG. 4 shows two ERP processes 410, 412 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 414 (e.g., Amazon S3, Amazon EMR, otherHadoop® Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 416. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 410, 412indicate optional additional ERP processes of the data intake and querysystem 108. An ERP process may be a computer process that is initiatedor spawned by the search head 210 and is executed by the search dataintake and query system 108. Alternatively or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to a SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 410, 412 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 410, 412 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 410, 412 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 410, 412 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices414, 416, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 404 may communicate with the data intake and query system108 through a network interface 420, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. Pat. No. 9,514,189, entitled “PROCESSING ASYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”, issued on 6 Dec.2016, each of which is hereby incorporated by reference in its entiretyfor all purposes.

3.6.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the machinedata obtained from the external data source) are provided to the searchhead, which can then process the results data (e.g., break the machinedata into events, timestamp it, filter it, etc.) and integrate theresults data with the results data from other external data sources,and/or from data stores of the search head. The search head performssuch processing and can immediately start returning interim (streamingmode) results to the user at the requesting client device;simultaneously, the search head is waiting for the ERP process toprocess the data it is retrieving from the external data source as aresult of the concurrently executing reporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the machined data or unprocesseddata necessary to respond to a search request) to the search head,enabling the search head to process the interim results and beginproviding to the client or search requester interim results that areresponsive to the query. Meanwhile, in this mixed mode, the ERP alsooperates concurrently in reporting mode, processing portions of machinedata in a manner responsive to the search query. Upon determining thatit has results from the reporting mode available to return to the searchhead, the ERP may halt processing in the mixed mode at that time (orsome later time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the machine data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return machine data tothe search head. As noted, the ERP process could be configured tooperate in streaming mode alone and return just the machine data for thesearch head to process in a way that is responsive to the searchrequest. Alternatively, the ERP process can be configured to operate inthe reporting mode only. Also, the ERP process can be configured tooperate in streaming mode and reporting mode concurrently, as described,with the ERP process stopping the transmission of streaming results tothe search head when the concurrently running reporting mode has caughtup and started providing results. The reporting mode does not requirethe processing of all machine data that is responsive to the searchquery request before the ERP process starts returning results; rather,the reporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

3.7. Data Ingestion

FIG. 5A is a flow chart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments. The data flow illustrated in FIG.5A is provided for illustrative purposes only; those skilled in the artwould understand that one or more of the steps of the processesillustrated in FIG. 5A may be removed or that the ordering of the stepsmay be changed. Furthermore, for the purposes of illustrating a clearexample, one or more particular system components are described in thecontext of performing various operations during each of the data flowstages. For example, a forwarder is described as receiving andprocessing machine data during an input phase; an indexer is describedas parsing and indexing machine data during parsing and indexing phases;and a search head is described as performing a search query during asearch phase. However, other system arrangements and distributions ofthe processing steps across system components may be used.

3.7.1. Input

At block 502, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In some embodiments, a forwarderreceives the raw data and may segment the data stream into “blocks”,possibly of a uniform data size, to facilitate subsequent processingsteps.

At block 504, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In someembodiments, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The data intake and query system allows forwarding of data from one dataintake and query instance to another, or even to a third-party system.The data intake and query system can employ different types offorwarders in a configuration.

In some embodiments, a forwarder may contain the essential componentsneeded to forward data. A forwarder can gather data from a variety ofinputs and forward the data to an indexer for indexing and searching. Aforwarder can also tag metadata (e.g., source, source type, host, etc.).

In some embodiments, a forwarder has the capabilities of theaforementioned forwarder as well as additional capabilities. Theforwarder can parse data before forwarding the data (e.g., can associatea time stamp with a portion of data and create an event, etc.) and canroute data based on criteria such as source or type of event. Theforwarder can also index data locally while forwarding the data toanother indexer.

3.7.2. Parsing

At block 506, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In some embodiments,to organize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries withinthe received data that indicate the portions of machine data for events.In general, these properties may include regular expression-based rulesor delimiter rules where, for example, event boundaries may be indicatedby predefined characters or character strings. These predefinedcharacters may include punctuation marks or other special charactersincluding, for example, carriage returns, tabs, spaces, line breaks,etc. If a source type for the data is unknown to the indexer, an indexermay infer a source type for the data by examining the structure of thedata. Then, the indexer can apply an inferred source type definition tothe data to create the events.

At block 508, the indexer determines a timestamp for each event. Similarto the process for parsing machine data, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data for the event, tointerpolate time values based on timestamps associated with temporallyproximate events, to create a timestamp based on a time the portion ofmachine data was received or generated, to use the timestamp of aprevious event, or use any other rules for determining timestamps.

At block 510, the indexer associates with each event one or moremetadata fields including a field containing the timestamp determinedfor the event. In some embodiments, a timestamp may be included in themetadata fields. These metadata fields may include any number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 504, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 512, an indexer may optionally apply one or moretransformations to data included in the events created at block 506. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to events may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

FIG. 5C illustrates an illustrative example of machine data can bestored in a data store in accordance with various disclosed embodiments.In other embodiments, machine data can be stored in a flat file in acorresponding bucket with an associated index file, such as a timeseries index or “TSIDX.” As such, the depiction of machine data andassociated metadata as rows and columns in the table of FIG. 5C ismerely illustrative and is not intended to limit the data format inwhich the machine data and metadata is stored in various embodimentsdescribed herein. In one particular embodiment, machine data can bestored in a compressed or encrypted formatted. In such embodiments, themachine data can be stored with or be associated with data thatdescribes the compression or encryption scheme with which the machinedata is stored. The information about the compression or encryptionscheme can be used to decompress or decrypt the machine data, and anymetadata with which it is stored, at search time.

As mentioned above, certain metadata, e.g., host 536, source 537, sourcetype 538, and timestamps 535 can be generated for each event, andassociated with a corresponding portion of machine data 539 when storingthe event data in a data store, e.g., data store 208. Any of themetadata can be extracted from the corresponding machine data, orsupplied or defined by an entity, such as a user or computer system. Themetadata fields can become part of or stored with the event. Note thatwhile the time-stamp metadata field can be extracted from the raw dataof each event, the values for the other metadata fields may bedetermined by the indexer based on information it receives pertaining tothe source of the data separate from the machine data.

While certain default or user-defined metadata fields can be extractedfrom the machine data for indexing purposes, all the machine data withinan event can be maintained in its original condition. As such, inembodiments in which the portion of machine data included in an event isunprocessed or otherwise unaltered, it is referred to herein as aportion of raw machine data. In other embodiments, the port of machinedata in an event can be processed or otherwise altered. As such, unlesscertain information needs to be removed for some reasons (e.g.extraneous information, confidential information), all the raw machinedata contained in an event can be preserved and saved in its originalform. Accordingly, the data store in which the event records are storedis sometimes referred to as a “raw record data store.” The raw recorddata store contains a record of the raw event data tagged with thevarious default fields.

In FIG. 5C, the first three rows of the table represent events 531, 532,and 533 and are related to a server access log that records requestsfrom multiple clients processed by a server, as indicated by entry of“access.log” in the source column 536.

In the example shown in FIG. 5C, each of the events 531-534 isassociated with a discrete request made from a client device. The rawmachine data generated by the server and extracted from a server accesslog can include the IP address of the client 540, the user id of theperson requesting the document 541, the time the server finishedprocessing the request 542, the request line from the client 543, thestatus code returned by the server to the client 545, the size of theobject returned to the client (in this case, the gif file requested bythe client) 546 and the time spent to serve the request in microseconds544. As seen in FIG. 5C, all the raw machine data retrieved from theserver access log is retained and stored as part of the correspondingevents, 1221, 1222, and 1223 in the data store.

Event 534 is associated with an entry in a server error log, asindicated by “error.log” in the source column 537 that records errorsthat the server encountered when processing a client request. Similar tothe events related to the server access log, all the raw machine data inthe error log file pertaining to event 534 can be preserved and storedas part of the event 534.

Saving minimally processed or unprocessed machine data in a data storeassociated with metadata fields in the manner similar to that shown inFIG. 5C is advantageous because it allows search of all the machine dataat search time instead of searching only previously specified andidentified fields or field-value pairs. As mentioned above, because datastructures used by various embodiments of the present disclosuremaintain the underlying raw machine data and use a late-binding schemafor searching the raw machines data, it enables a user to continueinvestigating and learn valuable insights about the raw data. In otherwords, the user is not compelled to know about all the fields ofinformation that will be needed at data ingestion time. As a user learnsmore about the data in the events, the user can continue to refine thelate-binding schema by defining new extraction rules, or modifying ordeleting existing extraction rules used by the system.3.7.3. Indexing

At blocks 514 and 516, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for events. To build akeyword index, at block 514, the indexer identifies a set of keywords ineach event. At block 516, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries for fieldname-value pairs found in events, where a field name-value pair caninclude a pair of keywords connected by a symbol, such as an equals signor colon. This way, events containing these field name-value pairs canbe quickly located. In some embodiments, fields can automatically begenerated for some or all of the field names of the field name-valuepairs at the time of indexing. For example, if the string“dest=10.0.1.2” is found in an event, a field named “dest” may becreated for the event, and assigned a value of “10.0.1.2”.

At block 518, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In some embodiments, the stored events are organizedinto “buckets,” where each bucket stores events associated with aspecific time range based on the timestamps associated with each event.This improves time-based searching, as well as allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk. In some embodiments, eachbucket may be associated with an identifier, a time range, and a sizeconstraint.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize the data retrieval process by searchingbuckets corresponding to time ranges that are relevant to a query.

In some embodiments, each indexer has a home directory and a colddirectory. The home directory of an indexer stores hot buckets and warmbuckets, and the cold directory of an indexer stores cold buckets. A hotbucket is a bucket that is capable of receiving and storing events. Awarm bucket is a bucket that can no longer receive events for storagebut has not yet been moved to the cold directory. A cold bucket is abucket that can no longer receive events and may be a bucket that waspreviously stored in the home directory. The home directory may bestored in faster memory, such as flash memory, as events may be activelywritten to the home directory, and the home directory may typicallystore events that are more frequently searched and thus are accessedmore frequently. The cold directory may be stored in slower and/orlarger memory, such as a hard disk, as events are no longer beingwritten to the cold directory, and the cold directory may typicallystore events that are not as frequently searched and thus are accessedless frequently. In some embodiments, an indexer may also have aquarantine bucket that contains events having potentially inaccurateinformation, such as an incorrect time stamp associated with the eventor a time stamp that appears to be an unreasonable time stamp for thecorresponding event. The quarantine bucket may have events from any timerange; as such, the quarantine bucket may always be searched at searchtime. Additionally, an indexer may store old, archived data in a frozenbucket that is not capable of being searched at search time. In someembodiments, a frozen bucket may be stored in slower and/or largermemory, such as a hard disk, and may be stored in offline and/or remotestorage.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. Pat. No. 9,130,971, entitled “SITE-BASEDSEARCH AFFINITY”, issued on 8 Sep. 2015, and in U.S. patent Ser. No.14/266,817, entitled “MULTI-SITE CLUSTERING”, issued on 1 Sep. 2015,each of which is hereby incorporated by reference in its entirety forall purposes.

As will be described in greater detail below with reference to, interalia, FIGS. 18-49, some functionality of the indexer can be handled bydifferent components of the system. For example, in some cases, theindexer indexes semi-processed, or cooked data (e.g., data that has beenparsed and/or had some fields determined for it), and stores the resultsin common storage.

FIG. 5B is a block diagram of an example data store 501 that includes adirectory for each index (or partition) that contains a portion of datamanaged by an indexer. FIG. 5B further illustrates details of anembodiment of an inverted index 507B and an event reference array 515associated with inverted index 507B.

The data store 501 can correspond to a data store 208 that stores eventsmanaged by an indexer 206 or can correspond to a different data storeassociated with an indexer 206. In the illustrated embodiment, the datastore 501 includes a _main directory 503 associated with a _main indexand a test directory 505 associated with a _test index. However, thedata store 501 can include fewer or more directories. In someembodiments, multiple indexes can share a single directory or allindexes can share a common directory. Additionally, although illustratedas a single data store 501, it will be understood that the data store501 can be implemented as multiple data stores storing differentportions of the information shown in FIG. 5B. For example, a singleindex or partition can span multiple directories or multiple datastores, and can be indexed or searched by multiple correspondingindexers.

In the illustrated embodiment of FIG. 5B, the index-specific directories503 and 505 include inverted indexes 507A, 507B and 509A, 509B,respectively. The inverted indexes 507A . . . 507B, and 509A . . . 509Bcan be keyword indexes or field-value pair indexes described herein andcan include less or more information that depicted in FIG. 5B.

In some embodiments, each inverted index 507A . . . 507B, and 509A . . .509B can correspond to a distinct time-series bucket that is managed bythe indexer 206 and that contains events corresponding to the relevantindex (e.g., _main index, _test index). As such, each inverted index cancorrespond to a particular range of time for an index. Additional files,such as high performance indexes for each time-series bucket of anindex, can also be stored in the same directory as the inverted indexes507A . . . 507B, and 509A . . . 509B. In some embodiments inverted index507A . . . 507B, and 509A . . . 509B can correspond to multipletime-series buckets or inverted indexes 507A . . . 507B, and 509A . . .509B can correspond to a single time-series bucket.

Each inverted index 507A . . . 507B, and 509A . . . 509B can include oneor more entries, such as keyword (or token) entries or field-value pairentries. Furthermore, in certain embodiments, the inverted indexes 507A. . . 507B, and 509A . . . 509B can include additional information, suchas a time range 523 associated with the inverted index or an indexidentifier 525 identifying the index associated with the inverted index507A . . . 507B, and 509A . . . 509B. However, each inverted index 507A. . . 507B, and 509A . . . 509B can include less or more informationthan depicted.

Token entries, such as token entries 511 illustrated in inverted index507B, can include a token 511A (e.g., “error,” “itemID,” etc.) and eventreferences 511B indicative of events that include the token. Forexample, for the token “error,” the corresponding token entry includesthe token “error” and an event reference, or unique identifier, for eachevent stored in the corresponding time-series bucket that includes thetoken “error.” In the illustrated embodiment of FIG. 5B, the error tokenentry includes the identifiers 3, 5, 6, 8, 11, and 12 corresponding toevents managed by the indexer 206 and associated with the index _main503 that are located in the time-series bucket associated with theinverted index 507B.

In some cases, some token entries can be default entries, automaticallydetermined entries, or user specified entries. In some embodiments, theindexer 206 can identify each word or string in an event as a distincttoken and generate a token entry for it. In some cases, the indexer 206can identify the beginning and ending of tokens based on punctuation,spaces, as described in greater detail herein. In certain cases, theindexer 206 can rely on user input or a configuration file to identifytokens for token entries 511, etc. It will be understood that anycombination of token entries can be included as a default, automaticallydetermined, a or included based on user-specified criteria.

Similarly, field-value pair entries, such as field-value pair entries513 shown in inverted index 507B, can include a field-value pair 513Aand event references 513B indicative of events that include a fieldvalue that corresponds to the field-value pair. For example, for afield-value pair sourcetype::sendmail, a field-value pair entry wouldinclude the field-value pair sourcetype::sendmail and a uniqueidentifier, or event reference, for each event stored in thecorresponding time-series bucket that includes a sendmail sourcetype.

In some cases, the field-value pair entries 513 can be default entries,automatically determined entries, or user specified entries. As anon-limiting example, the field-value pair entries for the fields host,source, sourcetype can be included in the inverted indexes 507A . . .507B, and 509A . . . 509B as a default. As such, all of the invertedindexes 507A . . . 507B, and 509A . . . 509B can include field-valuepair entries for the fields host, source, sourcetype. As yet anothernon-limiting example, the field-value pair entries for the IP_addressfield can be user specified and may only appear in the inverted index507B based on user-specified criteria. As another non-limiting example,as the indexer indexes the events, it can automatically identifyfield-value pairs and create field-value pair entries. For example,based on the indexers review of events, it can identify IP_address as afield in each event and add the IP_address field-value pair entries tothe inverted index 507B. It will be understood that any combination offield-value pair entries can be included as a default, automaticallydetermined, or included based on user-specified criteria.

Each unique identifier 517, or event reference, can correspond to aunique event located in the time series bucket. However, the same eventreference can be located in multiple entries. For example if an eventhas a sourcetype splunkd, host www1 and token “warning,” then the uniqueidentifier for the event will appear in the field-value pair entriessourcetype::splunkd and host::www1, as well as the token entry“warning.” With reference to the illustrated embodiment of FIG. 5B andthe event that corresponds to the event reference 3, the event reference3 is found in the field-value pair entries 513 host::hostA,source::sourceB, sourcetype::sourcetypeA, and IP_address::91.205.189.15indicating that the event corresponding to the event reference 3 is fromhostA, sourceB, of sourcetypeA, and includes 91.205.189.15 in the eventdata.

For some fields, the unique identifier is located in only onefield-value pair entry for a particular field. For example, the invertedindex may include four sourcetype field-value pair entries correspondingto four different sourcetypes of the events stored in a bucket (e.g.,sourcetypes: sendmail, splunkd, web_access, and web_service). Withinthose four sourcetype field-value pair entries, an identifier for aparticular event may appear in only one of the field-value pair entries.With continued reference to the example illustrated embodiment of FIG.5B, since the event reference 7 appears in the field-value pair entrysourcetype::sourcetypeA, then it does not appear in the otherfield-value pair entries for the sourcetype field, includingsourcetype::sourcetypeB, sourcetype::sourcetypeC, andsourcetype::sourcetypeD.

The event references 517 can be used to locate the events in thecorresponding bucket. For example, the inverted index can include, or beassociated with, an event reference array 515. The event reference array515 can include an array entry 517 for each event reference in theinverted index 507B. Each array entry 517 can include locationinformation 519 of the event corresponding to the unique identifier(non-limiting example: seek address of the event), a timestamp 521associated with the event, or additional information regarding the eventassociated with the event reference, etc.

For each token entry 511 or field-value pair entry 513, the eventreference 501B or unique identifiers can be listed in chronologicalorder or the value of the event reference can be assigned based onchronological data, such as a timestamp associated with the eventreferenced by the event reference. For example, the event reference 1 inthe illustrated embodiment of FIG. 5B can correspond to thefirst-in-time event for the bucket, and the event reference 12 cancorrespond to the last-in-time event for the bucket. However, the eventreferences can be listed in any order, such as reverse chronologicalorder, ascending order, descending order, or some other order, etc.Further, the entries can be sorted. For example, the entries can besorted alphabetically (collectively or within a particular group), byentry origin (e.g., default, automatically generated, user-specified,etc.), by entry type (e.g., field-value pair entry, token entry, etc.),or chronologically by when added to the inverted index, etc. In theillustrated embodiment of FIG. 5B, the entries are sorted first by entrytype and then alphabetically.

As a non-limiting example of how the inverted indexes 507A . . . 507B,and 509A . . . 509B can be used during a data categorization requestcommand, the indexers can receive filter criteria indicating data thatis to be categorized and categorization criteria indicating how the datais to be categorized. Example filter criteria can include, but is notlimited to, indexes (or partitions), hosts, sources, sourcetypes, timeranges, field identifier, keywords, etc.

Using the filter criteria, the indexer identifies relevant invertedindexes to be searched. For example, if the filter criteria includes aset of partitions, the indexer can identify the inverted indexes storedin the directory corresponding to the particular partition as relevantinverted indexes. Other means can be used to identify inverted indexesassociated with a partition of interest. For example, in someembodiments, the indexer can review an entry in the inverted indexes,such as an index-value pair entry 513 to determine if a particularinverted index is relevant. If the filter criteria does not identify anypartition, then the indexer can identify all inverted indexes managed bythe indexer as relevant inverted indexes.

Similarly, if the filter criteria includes a time range, the indexer canidentify inverted indexes corresponding to buckets that satisfy at leasta portion of the time range as relevant inverted indexes. For example,if the time range is last hour then the indexer can identify allinverted indexes that correspond to buckets storing events associatedwith timestamps within the last hour as relevant inverted indexes.

When used in combination, an index filter criterion specifying one ormore partitions and a time range filter criterion specifying aparticular time range can be used to identify a subset of invertedindexes within a particular directory (or otherwise associated with aparticular partition) as relevant inverted indexes. As such, the indexercan focus the processing to only a subset of the total number ofinverted indexes that the indexer manages.

Once the relevant inverted indexes are identified, the indexer canreview them using any additional filter criteria to identify events thatsatisfy the filter criteria. In some cases, using the known location ofthe directory in which the relevant inverted indexes are located, theindexer can determine that any events identified using the relevantinverted indexes satisfy an index filter criterion. For example, if thefilter criteria includes a partition main, then the indexer candetermine that any events identified using inverted indexes within thepartition main directory (or otherwise associated with the partitionmain) satisfy the index filter criterion.

Furthermore, based on the time range associated with each invertedindex, the indexer can determine that that any events identified using aparticular inverted index satisfies a time range filter criterion. Forexample, if a time range filter criterion is for the last hour and aparticular inverted index corresponds to events within a time range of50 minutes ago to 35 minutes ago, the indexer can determine that anyevents identified using the particular inverted index satisfy the timerange filter criterion. Conversely, if the particular inverted indexcorresponds to events within a time range of 59 minutes ago to 62minutes ago, the indexer can determine that some events identified usingthe particular inverted index may not satisfy the time range filtercriterion.

Using the inverted indexes, the indexer can identify event references(and therefore events) that satisfy the filter criteria. For example, ifthe token “error” is a filter criterion, the indexer can track all eventreferences within the token entry “error.” Similarly, the indexer canidentify other event references located in other token entries orfield-value pair entries that match the filter criteria. The system canidentify event references located in all of the entries identified bythe filter criteria. For example, if the filter criteria include thetoken “error” and field-value pair sourcetype::web_ui, the indexer cantrack the event references found in both the token entry “error” and thefield-value pair entry sourcetype::web_ui. As mentioned previously, insome cases, such as when multiple values are identified for a particularfilter criterion (e.g., multiple sources for a source filter criterion),the system can identify event references located in at least one of theentries corresponding to the multiple values and in all other entriesidentified by the filter criteria. The indexer can determine that theevents associated with the identified event references satisfy thefilter criteria.

In some cases, the indexer can further consult a timestamp associatedwith the event reference to determine whether an event satisfies thefilter criteria. For example, if an inverted index corresponds to a timerange that is partially outside of a time range filter criterion, thenthe indexer can consult a timestamp associated with the event referenceto determine whether the corresponding event satisfies the time rangecriterion. In some embodiments, to identify events that satisfy a timerange, the indexer can review an array, such as the event referencearray 1614 that identifies the time associated with the events.Furthermore, as mentioned above using the known location of thedirectory in which the relevant inverted indexes are located (or otherindex identifier), the indexer can determine that any events identifiedusing the relevant inverted indexes satisfy the index filter criterion.

In some cases, based on the filter criteria, the indexer reviews anextraction rule. In certain embodiments, if the filter criteria includesa field name that does not correspond to a field-value pair entry in aninverted index, the indexer can review an extraction rule, which may belocated in a configuration file, to identify a field that corresponds toa field-value pair entry in the inverted index.

For example, the filter criteria includes a field name “sessionID” andthe indexer determines that at least one relevant inverted index doesnot include a field-value pair entry corresponding to the field namesessionID, the indexer can review an extraction rule that identifies howthe sessionID field is to be extracted from a particular host, source,or sourcetype (implicitly identifying the particular host, source, orsourcetype that includes a sessionID field). The indexer can replace thefield name “sessionID” in the filter criteria with the identified host,source, or sourcetype. In some cases, the field name “sessionID” may beassociated with multiples hosts, sources, or sourcetypes, in which case,all identified hosts, sources, and sourcetypes can be added as filtercriteria. In some cases, the identified host, source, or sourcetype canreplace or be appended to a filter criterion, or be excluded. Forexample, if the filter criteria includes a criterion for source S1 andthe “sessionID” field is found in source S2, the source S2 can replaceS1 in the filter criteria, be appended such that the filter criteriaincludes source S1 and source S2, or be excluded based on the presenceof the filter criterion source S1. If the identified host, source, orsourcetype is included in the filter criteria, the indexer can thenidentify a field-value pair entry in the inverted index that includes afield value corresponding to the identity of the particular host,source, or sourcetype identified using the extraction rule.

Once the events that satisfy the filter criteria are identified, thesystem, such as the indexer 206 can categorize the results based on thecategorization criteria. The categorization criteria can includecategories for grouping the results, such as any combination ofpartition, source, sourcetype, or host, or other categories or fields asdesired.

The indexer can use the categorization criteria to identifycategorization criteria-value pairs or categorization criteria values bywhich to categorize or group the results. The categorizationcriteria-value pairs can correspond to one or more field-value pairentries stored in a relevant inverted index, one or more index-valuepairs based on a directory in which the inverted index is located or anentry in the inverted index (or other means by which an inverted indexcan be associated with a partition), or other criteria-value pair thatidentifies a general category and a particular value for that category.The categorization criteria values can correspond to the value portionof the categorization criteria-value pair.

As mentioned, in some cases, the categorization criteria-value pairs cancorrespond to one or more field-value pair entries stored in therelevant inverted indexes. For example, the categorizationcriteria-value pairs can correspond to field-value pair entries of host,source, and sourcetype (or other field-value pair entry as desired). Forinstance, if there are ten different hosts, four different sources, andfive different sourcetypes for an inverted index, then the invertedindex can include ten host field-value pair entries, four sourcefield-value pair entries, and five sourcetype field-value pair entries.The indexer can use the nineteen distinct field-value pair entries ascategorization criteria-value pairs to group the results.

Specifically, the indexer can identify the location of the eventreferences associated with the events that satisfy the filter criteriawithin the field-value pairs, and group the event references based ontheir location. As such, the indexer can identify the particular fieldvalue associated with the event corresponding to the event reference.For example, if the categorization criteria include host and sourcetype,the host field-value pair entries and sourcetype field-value pairentries can be used as categorization criteria-value pairs to identifythe specific host and sourcetype associated with the events that satisfythe filter criteria.

In addition, as mentioned, categorization criteria-value pairs cancorrespond to data other than the field-value pair entries in therelevant inverted indexes. For example, if partition or index is used asa categorization criterion, the inverted indexes may not includepartition field-value pair entries. Rather, the indexer can identify thecategorization criteria-value pair associated with the partition basedon the directory in which an inverted index is located, information inthe inverted index, or other information that associates the invertedindex with the partition, etc. As such a variety of methods can be usedto identify the categorization criteria-value pairs from thecategorization criteria.

Accordingly based on the categorization criteria (and categorizationcriteria-value pairs), the indexer can generate groupings based on theevents that satisfy the filter criteria. As a non-limiting example, ifthe categorization criteria includes a partition and sourcetype, thenthe groupings can correspond to events that are associated with eachunique combination of partition and sourcetype. For instance, if thereare three different partitions and two different sourcetypes associatedwith the identified events, then the six different groups can be formed,each with a unique partition value-sourcetype value combination.Similarly, if the categorization criteria includes partition,sourcetype, and host and there are two different partitions, threesourcetypes, and five hosts associated with the identified events, thenthe indexer can generate up to thirty groups for the results thatsatisfy the filter criteria. Each group can be associated with a uniquecombination of categorization criteria-value pairs (e.g., uniquecombinations of partition value sourcetype value, and host value).

In addition, the indexer can count the number of events associated witheach group based on the number of events that meet the uniquecombination of categorization criteria for a particular group (or matchthe categorization criteria-value pairs for the particular group). Withcontinued reference to the example above, the indexer can count thenumber of events that meet the unique combination of partition,sourcetype, and host for a particular group.

Each indexer communicates the groupings to the search head. The searchhead can aggregate the groupings from the indexers and provide thegroupings for display. In some cases, the groups are displayed based onat least one of the host, source, sourcetype, or partition associatedwith the groupings. In some embodiments, the search head can furtherdisplay the groups based on display criteria, such as a display order ora sort order as described in greater detail above.

As a non-limiting example and with reference to FIG. 5B, consider arequest received by an indexer 206 that includes the following filtercriteria: keyword=error, partition=_main, time range=3/1/1716:22.00.000-16:28.00.000, sourcetype=sourcetypeC, host=hostB, and thefollowing categorization criteria: source.

Based on the above criteria, the indexer 206 identifies _main directory503 and can ignore _test directory 505 and any other partition-specificdirectories. The indexer determines that inverted partition 507B is arelevant partition based on its location within the _main directory 503and the time range associated with it. For sake of simplicity in thisexample, the indexer 206 determines that no other inverted indexes inthe _main directory 503, such as inverted index 507A satisfy the timerange criterion.

Having identified the relevant inverted index 507B, the indexer reviewsthe token entries 511 and the field-value pair entries 513 to identifyevent references, or events, that satisfy all of the filter criteria.

With respect to the token entries 511, the indexer can review the errortoken entry and identify event references 3, 5, 6, 8, 11, 12, indicatingthat the term “error” is found in the corresponding events. Similarly,the indexer can identify event references 4, 5, 6, 8, 9, 10, 11 in thefield-value pair entry sourcetype::sourcetypeC and event references 2,5, 6, 8, 10, 11 in the field-value pair entry host::hostB. As the filtercriteria did not include a source or an IP_address field-value pair, theindexer can ignore those field-value pair entries.

In addition to identifying event references found in at least one tokenentry or field-value pair entry (e.g., event references 3, 4, 5, 6, 8,9, 10, 11, 12), the indexer can identify events (and corresponding eventreferences) that satisfy the time range criterion using the eventreference array 1614 (e.g., event references 2, 3, 4, 5, 6, 7, 8, 9,10). Using the information obtained from the inverted index 507B(including the event reference array 515), the indexer 206 can identifythe event references that satisfy all of the filter criteria (e.g.,event references 5, 6, 8).

Having identified the events (and event references) that satisfy all ofthe filter criteria, the indexer 206 can group the event referencesusing the received categorization criteria (source). In doing so, theindexer can determine that event references 5 and 6 are located in thefield-value pair entry source::sourceD (or have matching categorizationcriteria-value pairs) and event reference 8 is located in thefield-value pair entry source::sourceC. Accordingly, the indexer cangenerate a sourceC group having a count of one corresponding toreference 8 and a sourceD group having a count of two corresponding toreferences 5 and 6. This information can be communicated to the searchhead. In turn the search head can aggregate the results from the variousindexers and display the groupings. As mentioned above, in someembodiments, the groupings can be displayed based at least in part onthe categorization criteria, including at least one of host, source,sourcetype, or partition.

It will be understood that a change to any of the filter criteria orcategorization criteria can result in different groupings. As a onenon-limiting example, a request received by an indexer 206 that includesthe following filter criteria: partition=_main, time range=3/1/17 3/1/1716:21:20.000-16:28:17.000, and the following categorization criteria:host, source, sourcetype would result in the indexer identifying eventreferences 1-12 as satisfying the filter criteria. The indexer wouldthen generate up to 24 groupings corresponding to the 24 differentcombinations of the categorization criteria-value pairs, including host(hostA, hostB), source (sourceA, sourceB, sourceC, sourceD), andsourcetype (sourcetypeA, sourcetypeB, sourcetypeC). However, as thereare only twelve events identifiers in the illustrated embodiment andsome fall into the same grouping, the indexer generates eight groups andcounts as follows:

Group 1 (hostA, sourceA, sourcetypeA): 1 (event reference 7).

Group 2 (hostA, sourceA, sourcetypeB): 2 (event references 1, 12)

Group 3 (hostA, sourceA, sourcetypeC): 1 (event reference 4)

Group 4 (hostA, sourceB, sourcetypeA): 1 (event reference 3)

Group 5 (hostA, sourceB, sourcetypeC): 1 (event reference 9)

Group 6 (hostB, sourceC, sourcetypeA): 1 (event reference 2)

Group 7 (hostB, sourceC, sourcetypeC): 2 (event references 8, 11)

Group 8 (hostB, sourceD, sourcetypeC): 3 (event references 5, 6, 10)

As noted, each group has a unique combination of categorizationcriteria-value pairs or categorization criteria values. The indexercommunicates the groups to the search head for aggregation with resultsreceived from other indexers. In communicating the groups to the searchhead, the indexer can include the categorization criteria-value pairsfor each group and the count. In some embodiments, the indexer caninclude more or less information. For example, the indexer can includethe event references associated with each group and other identifyinginformation, such as the indexer or inverted index used to identify thegroups.

As another non-limiting examples, a request received by an indexer 206that includes the following filter criteria: partition=_main, timerange=3/1/17 3/1/17 16:21:20.000-16:28:17.000, source=sourceA, sourceD,and keyword=itemID and the following categorization criteria: host,source, sourcetype would result in the indexer identifying eventreferences 4, 7, and 10 as satisfying the filter criteria, and generatethe following groups:

Group 1 (hostA, sourceA, sourcetypeC): 1 (event reference 4)

Group 2 (hostA, sourceA, sourcetypeA): 1 (event reference 7)

Group 3 (hostB, sourceD, sourcetypeC): 1 (event references 10)

The indexer communicates the groups to the search head for aggregationwith results received from other indexers. As will be understand thereare myriad ways for filtering and categorizing the events and eventreferences. For example, the indexer can review multiple invertedindexes associated with an partition or review the inverted indexes ofmultiple partitions, and categorize the data using any one or anycombination of partition, host, source, sourcetype, or other category,as desired.

Further, if a user interacts with a particular group, the indexer canprovide additional information regarding the group. For example, theindexer can perform a targeted search or sampling of the events thatsatisfy the filter criteria and the categorization criteria for theselected group, also referred to as the filter criteria corresponding tothe group or filter criteria associated with the group.

In some cases, to provide the additional information, the indexer relieson the inverted index. For example, the indexer can identify the eventreferences associated with the events that satisfy the filter criteriaand the categorization criteria for the selected group and then use theevent reference array 515 to access some or all of the identifiedevents. In some cases, the categorization criteria values orcategorization criteria-value pairs associated with the group becomepart of the filter criteria for the review.

With reference to FIG. 5B for instance, suppose a group is displayedwith a count of six corresponding to event references 4, 5, 6, 8, 10, 11(i.e., event references 4, 5, 6, 8, 10, 11 satisfy the filter criteriaand are associated with matching categorization criteria values orcategorization criteria-value pairs) and a user interacts with the group(e.g., selecting the group, clicking on the group, etc.). In response,the search head communicates with the indexer to provide additionalinformation regarding the group.

In some embodiments, the indexer identifies the event referencesassociated with the group using the filter criteria and thecategorization criteria for the group (e.g., categorization criteriavalues or categorization criteria-value pairs unique to the group).Together, the filter criteria and the categorization criteria for thegroup can be referred to as the filter criteria associated with thegroup. Using the filter criteria associated with the group, the indexeridentifies event references 4, 5, 6, 8, 10, 11.

Based on a sampling criteria, discussed in greater detail above, theindexer can determine that it will analyze a sample of the eventsassociated with the event references 4, 5, 6, 8, 10, 11. For example,the sample can include analyzing event data associated with the eventreferences 5, 8, 10. In some embodiments, the indexer can use the eventreference array 515 to access the event data associated with the eventreferences 5, 8, 10. Once accessed, the indexer can compile the relevantinformation and provide it to the search head for aggregation withresults from other indexers. By identifying events and sampling eventdata using the inverted indexes, the indexer can reduce the amount ofactual data this is analyzed and the number of events that are accessedin order to generate the summary of the group and provide a response inless time.

3.8. Query Processing

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments. At block 602, a search head receives a search queryfrom a client. At block 604, the search head analyzes the search queryto determine what portion(s) of the query can be delegated to indexersand what portions of the query can be executed locally by the searchhead. At block 606, the search head distributes the determined portionsof the query to the appropriate indexers. In some embodiments, a searchhead cluster may take the place of an independent search head where eachsearch head in the search head cluster coordinates with peer searchheads in the search head cluster to schedule jobs, replicate searchresults, update configurations, fulfill search requests, etc. In someembodiments, the search head (or each search head) communicates with amaster node (also known as a cluster master, not shown in FIG. 2) thatprovides the search head with a list of indexers to which the searchhead can distribute the determined portions of the query. The masternode maintains a list of active indexers and can also designate whichindexers may have responsibility for responding to queries over certainsets of events. A search head may communicate with the master nodebefore the search head distributes queries to indexers to discover theaddresses of active indexers.

At block 608, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 608 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In some embodiments, one or morerules for extracting field values may be specified as part of a sourcetype definition in a configuration file. The indexers may then eithersend the relevant events back to the search head, or use the events todetermine a partial result, and send the partial result back to thesearch head.

At block 610, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Insome examples, the results of the query are indicative of performance orsecurity of the IT environment and may help improve the performance ofcomponents in the IT environment. This final result may comprisedifferent types of data depending on what the query requested. Forexample, the results can include a listing of matching events returnedby the query, or some type of visualization of the data from thereturned events. In another example, the final result can include one ormore calculated values derived from the matching events.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries, which may beparticularly helpful for queries that are performed on a periodic basis.

As will be described in greater detail below with reference to, interalia, FIGS. 18-49, some functionality of the search head or indexers canbe handled by different components of the system or removed altogether.For example, in some cases, a query coordinator analyzes the query,identifies dataset sources to be accessed, generates subqueries forexecution by dataset sources, such as indexers, collects partial resultsto produce a final result and returns the final results to the searchhead for delivery to a client device or delivers the final results tothe client device without the search head. In some cases, results fromdataset sources, such as the indexers, are communicated to nodes, whichfurther process the data, and communicate the results of the processingto the query coordinator, etc. In some embodiments, the search headspawns a search process, which communicates the query to a searchprocess master. The search process master can communicate the query tothe query coordinator for processing and execution.

In addition, in some embodiments, the indexers are not involved insearch operations or only search some data, such as data in hot buckets,etc. For example, nodes can perform the search functionality describedherein with respect to indexers. For example, nodes can use late-bindingschema to extract values for specified fields from events at the timethe query is processed and/or use one or more rules specified as part ofa source type definition in a configuration file for extracting fieldvalues, etc. Furthermore, in some embodiments, nodes can perform searchoperations on data in common storage or found in other dataset sources,such as external data stores, query acceleration data stores, ingesteddata buffers, etc.

3.9. Pipelined Search Language

Various embodiments of the present disclosure can be implemented using,or in conjunction with, a pipelined command language. A pipelinedcommand language is a language in which a set of inputs or data isoperated on by a first command in a sequence of commands, and thensubsequent commands in the order they are arranged in the sequence. Suchcommands can include any type of functionality for operating on data,such as retrieving, searching, filtering, aggregating, processing,transmitting, and the like. As described herein, a query can thus beformulated in a pipelined command language and include any number ofordered or unordered commands for operating on data.

Splunk Processing Language (SPL) is an example of a pipelined commandlanguage in which a set of inputs or data is operated on by any numberof commands in a particular sequence. A sequence of commands, or commandsequence, can be formulated such that the order in which the commandsare arranged defines the order in which the commands are applied to aset of data or the results of an earlier executed command. For example,a first command in a command sequence can operate to search or filterfor specific data in particular set of data. The results of the firstcommand can then be passed to another command listed later in thecommand sequence for further processing.

In various embodiments, a query can be formulated as a command sequencedefined in a command line of a search UI. In some embodiments, a querycan be formulated as a sequence of SPL commands. Some or all of the SPLcommands in the sequence of SPL commands can be separated from oneanother by a pipe symbol “1”. In such embodiments, a set of data, suchas a set of events, can be operated on by a first SPL command in thesequence, and then a subsequent SPL command following a pipe symbol “1”after the first SPL command operates on the results produced by thefirst SPL command or other set of data, and so on for any additional SPLcommands in the sequence. As such, a query formulated using SPLcomprises a series of consecutive commands that are delimited by pipe“1” characters. The pipe character indicates to the system that theoutput or result of one command (to the left of the pipe) should be usedas the input for one of the subsequent commands (to the right of thepipe). This enables formulation of queries defined by a pipeline ofsequenced commands that refines or enhances the data at each step alongthe pipeline until the desired results are attained. Accordingly,various embodiments described herein can be implemented with SplunkProcessing Language (SPL) used in conjunction with the SPLUNK®ENTERPRISE system.

While a query can be formulated in many ways, a query can start with asearch command and one or more corresponding search terms at thebeginning of the pipeline. Such search terms can include any combinationof keywords, phrases, times, dates, Boolean expressions, fieldname-fieldvalue pairs, etc. that specify which results should be obtained from anindex. The results can then be passed as inputs into subsequent commandsin a sequence of commands by using, for example, a pipe character. Thesubsequent commands in a sequence can include directives for additionalprocessing of the results once it has been obtained from one or moreindexes. For example, commands may be used to filter unwantedinformation out of the results, extract more information, evaluate fieldvalues, calculate statistics, reorder the results, create an alert,create summary of the results, or perform some type of aggregationfunction. In some embodiments, the summary can include a graph, chart,metric, or other visualization of the data. An aggregation function caninclude analysis or calculations to return an aggregate value, such asan average value, a sum, a maximum value, a root mean square,statistical values, and the like.

Due to its flexible nature, use of a pipelined command language invarious embodiments is advantageous because it can perform “filtering”as well as “processing” functions. In other words, a single query caninclude a search command and search term expressions, as well asdata-analysis expressions. For example, a command at the beginning of aquery can perform a “filtering” step by retrieving a set of data basedon a condition (e.g., records associated with server response times ofless than 1 microsecond). The results of the filtering step can then bepassed to a subsequent command in the pipeline that performs a“processing” step (e.g. calculation of an aggregate value related to thefiltered events such as the average response time of servers withresponse times of less than 1 microsecond). Furthermore, the searchcommand can allow events to be filtered by keyword as well as fieldvalue criteria. For example, a search command can filter out all eventscontaining the word “warning” or filter out all events where a fieldvalue associated with a field “clientip” is “10.0.1.2.”

The results obtained or generated in response to a command in a querycan be considered a set of results data. The set of results data can bepassed from one command to another in any data format. In oneembodiment, the set of result data can be in the form of a dynamicallycreated table. Each command in a particular query can redefine the shapeof the table. In some implementations, an event retrieved from an indexin response to a query can be considered a row with a column for eachfield value. Columns contain basic information about the data and alsomay contain data that has been dynamically extracted at search time.

FIG. 6B provides a visual representation of the manner in which apipelined command language or query operates in accordance with thedisclosed embodiments. The query 630 can be inputted by the user into asearch. The query comprises a search, the results of which are piped totwo commands (namely, command 1 and command 2) that follow the searchstep.

Disk 622 represents the event data in the raw record data store.

When a user query is processed, a search step will precede other queriesin the pipeline in order to generate a set of events at block 640. Forexample, the query can comprise search terms “sourcetype=syslog ERROR”at the front of the pipeline as shown in FIG. 6B. Intermediate resultstable 624 shows fewer rows because it represents the subset of eventsretrieved from the index that matched the search terms“sourcetype=syslog ERROR” from search command 630. By way of furtherexample, instead of a search step, the set of events at the head of thepipeline may be generating by a call to a pre-existing inverted index(as will be explained later).

At block 642, the set of events generated in the first part of the querymay be piped to a query that searches the set of events for field-valuepairs or for keywords. For example, the second intermediate resultstable 626 shows fewer columns, representing the result of the topcommand, “top user” which summarizes the events into a list of the top10 users and displays the user, count, and percentage.

Finally, at block 644, the results of the prior stage can be pipelinedto another stage where further filtering or processing of the data canbe performed, e.g., preparing the data for display purposes, filteringthe data based on a condition, performing a mathematical calculationwith the data, etc. As shown in FIG. 6B, the “fields-percent” part ofcommand 630 removes the column that shows the percentage, thereby,leaving a final results table 628 without a percentage column. Indifferent embodiments, other query languages, such as the StructuredQuery Language (“SQL”), can be used to create a query. In someembodiments, each stage can correspond to a search phase or layer in aDAG. The processing performed in each stage can be handled by one ormore partitions allocated to each stage.

3.10. Field Extraction

The search head 210 allows users to search and visualize eventsgenerated from machine data received from homogenous data sources. Thesearch head 210 also allows users to search and visualize eventsgenerated from machine data received from heterogeneous data sources.The search head 210 includes various mechanisms, which may additionallyreside in an indexer 206, for processing a query. A query language maybe used to create a query, such as any suitable pipelined querylanguage. For example, Splunk Processing Language (SPL) can be utilizedto make a query. SPL is a pipelined search language in which a set ofinputs is operated on by a first command in a command line, and then asubsequent command following the pipe symbol “1” operates on the resultsproduced by the first command, and so on for additional commands. Otherquery languages, such as the Structured Query Language (“SQL”), can beused to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for fields in the events beingsearched. The search head 210 obtains extraction rules that specify howto extract a value for fields from an event. Extraction rules cancomprise regex rules that specify how to extract values for the fieldscorresponding to the extraction rules. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, anextraction rule may truncate a character string or convert the characterstring into a different data format. In some cases, the query itself canspecify one or more extraction rules.

The search head 210 can apply the extraction rules to events that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the portions of machine datain the events and examining the data for one or more patterns ofcharacters, numbers, delimiters, etc., that indicate where the fieldbegins and, optionally, ends.

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, FIGS. 18-49, some functionality of thesearch head or indexers can be handled by different components of thesystem or removed altogether. For example, in some cases, a querycoordinator or nodes use extraction rules to extract values for fieldsin the events being searched. The query coordinator or nodes obtainextraction rules that specify how to extract a value for fields from anevent, etc., and apply the extraction rules to events that it receivesfrom indexers, common storage, ingested data buffers, query accelerationdata stores, or other dataset sources.

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments. In this example, a usersubmits an order for merchandise using a vendor's shopping applicationprogram 701 running on the user's system. In this example, the order wasnot delivered to the vendor's server due to a resource exception at thedestination server that is detected by the middleware code 702. The userthen sends a message to the customer support server 703 to complainabout the order failing to complete. The three systems 701, 702, and 703are disparate systems that do not have a common logging format. Theorder application 701 sends log data 704 to the data intake and querysystem in one format, the middleware code 702 sends error log data 705in a second format, and the support server 703 sends log data 706 in athird format.

Using the log data received at one or more indexers 206 from the threesystems, the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of the systems.There is a semantic relationship between the customer ID field valuesgenerated by the three systems. The search head 210 requests events fromthe one or more indexers 206 to gather relevant events from the threesystems. The search head 210 then applies extraction rules to the eventsin order to extract field values that it can correlate. The search headmay apply a different extraction rule to each set of events from eachsystem when the event format differs among systems. In this example, theuser interface can display to the administrator the events correspondingto the common customer ID field values 707, 708, and 709, therebyproviding the administrator with insight into a customer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, avisualization (e.g., a graph or chart) generated from the values, andthe like.

The search system enables users to run queries against the stored datato retrieve events that meet criteria specified in a query, such ascontaining certain keywords or having specific values in defined fields.FIG. 7B illustrates the manner in which keyword searches and fieldsearches are processed in accordance with disclosed embodiments.

If a user inputs a search query into search bar 1401 that includes onlykeywords (also known as “tokens”), e.g., the keyword “error” or“warning”, the query search engine of the data intake and query systemsearches for those keywords directly in the event data 722 stored in theraw record data store. Note that while FIG. 7B only illustrates fourevents, the raw record data store (corresponding to data store 208 inFIG. 2) may contain records for millions of events.

As disclosed above, an indexer can optionally generate a keyword indexto facilitate fast keyword searching for event data. The indexerincludes the identified keywords in an index, which associates eachstored keyword with reference pointers to events containing that keyword(or to locations within events where that keyword is located, otherlocation identifiers, etc.). When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword. For example, if the keyword“HTTP” was indexed by the indexer at index time, and the user searchesfor the keyword “HTTP”, events 713 to 715 will be identified based onthe results returned from the keyword index. As noted above, the indexcontains reference pointers to the events containing the keyword, whichallows for efficient retrieval of the relevant events from the rawrecord data store.

If a user searches for a keyword that has not been indexed by theindexer, the data intake and query system would nevertheless be able toretrieve the events by searching the event data for the keyword in theraw record data store directly as shown in FIG. 7B. For example, if auser searches for the keyword “frank”, and the name “frank” has not beenindexed at index time, the DATA INTAKE AND QUERY system will search theevent data directly and return the first event 713. Note that whetherthe keyword has been indexed at index time or not, in both cases the rawdata with the events 712 is accessed from the raw data record store toservice the keyword search. In the case where the keyword has beenindexed, the index will contain a reference pointer that will allow fora more efficient retrieval of the event data from the data store. If thekeyword has not been indexed, the search engine will need to searchthrough all the records in the data store to service the search.

In most cases, however, in addition to keywords, a user's search willalso include fields. The term “field” refers to a location in the eventdata containing one or more values for a specific data item. Often, afield is a value with a fixed, delimited position on a line, or a nameand value pair, where there is a single value to each field name. Afield can also be multivalued, that is, it can appear more than once inan event and have a different value for each appearance, e.g., emailaddress fields. Fields are searchable by the field name or fieldname-value pairs. Some examples of fields are “clientip” for IPaddresses accessing a web server, or the “From” and “To” fields in emailaddresses.

By way of further example, consider the search, “status=404”. Thissearch query finds events with “status” fields that have a value of“404.” When the search is run, the search engine does not look forevents with any other “status” value. It also does not look for eventscontaining other fields that share “404” as a value. As a result, thesearch returns a set of results that are more focused than if “404” hadbeen used in the search string as part of a keyword search. Note alsothat fields can appear in events as “key=value” pairs such as“user_name=Bob.” But in most cases, field values appear in fixed,delimited positions without identifying keys. For example, the datastore may contain events where the “user_name” value always appears byitself after the timestamp as illustrated by the following string: “Nov15 09:33:22 johnmedlock.”

The data intake and query system advantageously allows for search timefield extraction. In other words, fields can be extracted from the eventdata at search time using late-binding schema as opposed to at dataingestion time, which was a major limitation of the prior art systems.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

FIG. 7B illustrates the manner in which configuration files may be usedto configure custom fields at search time in accordance with thedisclosed embodiments. In response to receiving a search query, the dataintake and query system determines if the query references a “field.”For example, a query may request a list of events where the “clientip”field equals “127.0.0.1.” If the query itself does not specify anextraction rule and if the field is not a metadata field, e.g., time,host, source, source type, etc., then in order to determine anextraction rule, the search engine may, in one or more embodiments, needto locate configuration file 712 during the execution of the search asshown in FIG. 7B.

Configuration file 712 may contain extraction rules for all the variousfields that are not metadata fields, e.g., the “clientip” field. Theextraction rules may be inserted into the configuration file in avariety of ways. In some embodiments, the extraction rules can compriseregular expression rules that are manually entered in by the user.Regular expressions match patterns of characters in text and are usedfor extracting custom fields in text.

In one or more embodiments, as noted above, a field extractor may beconfigured to automatically generate extraction rules for certain fieldvalues in the events when the events are being created, indexed, orstored, or possibly at a later time. In one embodiment, a user may beable to dynamically create custom fields by highlighting portions of asample event that should be extracted as fields using a graphical userinterface. The system would then generate a regular expression thatextracts those fields from similar events and store the regularexpression as an extraction rule for the associated field in theconfiguration file 712.

In some embodiments, the indexers may automatically discover certaincustom fields at index time and the regular expressions for those fieldswill be automatically generated at index time and stored as part ofextraction rules in configuration file 712. For example, fields thatappear in the event data as “key=value” pairs may be automaticallyextracted as part of an automatic field discovery process. Note thatthere may be several other ways of adding field definitions toconfiguration files in addition to the methods discussed herein.

The search head 210 can apply the extraction rules derived fromconfiguration file 712 to event data that it receives from indexers 206.Indexers 206 may apply the extraction rules from the configuration fileto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

In one more embodiments, the extraction rule in configuration file 712will also need to define the type or set of events that the rule appliesto. Because the raw record data store will contain events from multipleheterogeneous sources, multiple events may contain the same fields indifferent locations because of discrepancies in the format of the datagenerated by the various sources. Furthermore, certain events may notcontain a particular field at all. For example, event 719 also contains“clientip” field, however, the “clientip” field is in a different formatfrom events 713-715. To address the discrepancies in the format andcontent of the different types of events, the configuration file willalso need to specify the set of events that an extraction rule appliesto, e.g., extraction rule 716 specifies a rule for filtering by the typeof event and contains a regular expression for parsing out the fieldvalue. Accordingly, each extraction rule will pertain to only aparticular type of event. If a particular field, e.g., “clientip” occursin multiple events, each of those types of events would need its owncorresponding extraction rule in the configuration file 712 and each ofthe extraction rules would comprise a different regular expression toparse out the associated field value. The most common way to categorizeevents is by source type because events generated by a particular sourcecan have the same format.

The field extraction rules stored in configuration file 712 performsearch-time field extractions. For example, for a query that requests alist of events with source type “access_combined” where the “clientip”field equals “127.0.0.1,” the query search engine would first locate theconfiguration file 712 to retrieve extraction rule 716 that would allowit to extract values associated with the “clientip” field from the eventdata 720 “where the source type is “access_combined. After the“clientip” field has been extracted from all the events comprising the“clientip” field where the source type is “access_combined,” the querysearch engine can then execute the field criteria by performing thecompare operation to filter out the events where the “clientip” fieldequals “127.0.0.1.” In the example shown in FIG. 7B, events 713-715would be returned in response to the user query. In this manner, thesearch engine can service queries containing field criteria in additionto queries containing keyword criteria (as explained above).

The configuration file can be created during indexing. It may either bemanually created by the user or automatically generated with certainpredetermined field extraction rules. As discussed above, the events maybe distributed across several indexers, wherein each indexer may beresponsible for storing and searching a subset of the events containedin a corresponding data store. In a distributed indexer system, eachindexer would need to maintain a local copy of the configuration filethat is synchronized periodically across the various indexers.

The ability to add schema to the configuration file at search timeresults in increased efficiency. A user can create new fields at searchtime and simply add field definitions to the configuration file. As auser learns more about the data in the events, the user can continue torefine the late-binding schema by adding new fields, deleting fields, ormodifying the field extraction rules in the configuration file for usethe next time the schema is used by the system. Because the data intakeand query system maintains the underlying raw data and uses late-bindingschema for searching the raw data, it enables a user to continueinvestigating and learn valuable insights about the raw data long afterdata ingestion time.

The ability to add multiple field definitions to the configuration fileat search time also results in increased flexibility. For example,multiple field definitions can be added to the configuration file tocapture the same field across events generated by different sourcetypes. This allows the data intake and query system to search andcorrelate data across heterogeneous sources flexibly and efficiently.

Further, by providing the field definitions for the queried fields atsearch time, the configuration file 712 allows the record data store 712to be field searchable. In other words, the raw record data store 712can be searched using keywords as well as fields, wherein the fields aresearchable name/value pairings that distinguish one event from anotherand can be defined in configuration file 712 using extraction rules. Incomparison to a search containing field names, a keyword search does notneed the configuration file and can search the event data directly asshown in FIG. 7B.

It should also be noted that any events filtered out by performing asearch-time field extraction using a configuration file can be furtherprocessed by directing the results of the filtering step to a processingstep using a pipelined search language. Using the prior example, a usercould pipeline the results of the compare step to an aggregate functionby asking the query search engine to count the number of events wherethe “clientip” field equals “127.0.0.1.”

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, FIGS. 18-49, some functionality of thesearch head or indexers can be handled by different components of thesystem or removed altogether. For example, in some cases, the data isstored in a dataset source, which may be an indexer (or data storecontrolled by an indexer) or may be a different type of dataset source,such as a common storage or external data source. In addition, a querycoordinator or node can request events from the indexers or otherdataset source, apply extraction rules and correlate, automaticallydiscover certain custom fields, etc., as described above.

3.11. Example Search Screen

FIG. 8A is an interface diagram of an example user interface for asearch screen 800, in accordance with example embodiments. Search screen800 includes a search bar 802 that accepts user input in the form of asearch string. It also includes a time range picker 812 that enables theuser to specify a time range for the search. For historical searches(e.g., searches based on a particular historical time range), the usercan select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday” or “last week.” For real-timesearches (e.g., searches whose results are based on data received inreal-time), the user can select the size of a time window to search forreal-time events. Search screen 800 also initially displays a “datasummary” dialog as is illustrated in FIG. 8B that enables the user toselect different sources for the events, such as by selecting specifichosts and log files.

After the search is executed, the search screen 800 in FIG. 8A candisplay the results through search results tabs 804, wherein searchresults tabs 804 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 8A displays a timeline graph 805 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. The events tab also displays anevents list 808 that enables a user to view the machine data in each ofthe returned events.

The events tab additionally displays a sidebar that is an interactivefield picker 806. The field picker 806 may be displayed to a user inresponse to the search being executed and allows the user to furtheranalyze the search results based on the fields in the events of thesearch results. The field picker 806 includes field names that referencefields present in the events in the search results. The field picker maydisplay any Selected Fields 820 that a user has pre-selected for display(e.g., host, source, sourcetype) and may also display any InterestingFields 822 that the system determines may be interesting to the userbased on pre-specified criteria (e.g., action, bytes, categoryid,clientip, date_hour, date_mday, date_minute, etc.). The field pickeralso provides an option to display field names for all the fieldspresent in the events of the search results using the All Fields control824.

Each field name in the field picker 806 has a value type identifier tothe left of the field name, such as value type identifier 826. A valuetype identifier identifies the type of value for the respective field,such as an “a” for fields that include literal values or a “#” forfields that include numerical values.

Each field name in the field picker also has a unique value count to theright of the field name, such as unique value count 828. The uniquevalue count indicates the number of unique values for the respectivefield in the events of the search results.

Each field name is selectable to view the events in the search resultsthat have the field referenced by that field name. For example, a usercan select the “host” field name, and the events shown in the eventslist 808 will be updated with events in the search results that have thefield that is reference by the field name “host.”

3.12. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge used to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.An object is defined by constraints and attributes. An object'sconstraints are search criteria that define the set of events to beoperated on by running a search having that search criteria at the timethe data model is selected. An object's attributes are the set of fieldsto be exposed for operating on that set of events generated by thesearch criteria.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Child objects inherit constraints andattributes from their parent objects and may have additional constraintsand attributes of their own. Child objects provide a way of filteringevents from parent objects. Because a child object may provide anadditional constraint in addition to the constraints it has inheritedfrom its parent object, the dataset it represents may be a subset of thedataset that its parent represents. For example, a first data modelobject may define a broad set of data pertaining to e-mail activitygenerally, and another data model object may define specific datasetswithin the broad dataset, such as a subset of the e-mail data pertainingspecifically to e-mails sent. For example, a user can simply select an“e-mail activity” data model object to access a dataset relating toe-mails generally (e.g., sent or received), or select an “e-mails sent”data model object (or data sub-model object) to access a datasetrelating to e-mails sent.

Because a data model object is defined by its constraints (e.g., a setof search criteria) and attributes (e.g., a set of fields), a data modelobject can be used to quickly search data to identify a set of eventsand to identify a set of fields to be associated with the set of events.For example, an “e-mails sent” data model object may specify a searchfor events relating to e-mails that have been sent, and specify a set offields that are associated with the events. Thus, a user can retrieveand use the “e-mails sent” data model object to quickly search sourcedata for events relating to sent e-mails, and may be provided with alisting of the set of fields relevant to the events in a user interfacescreen.

Examples of data models can include electronic mail, authentication,databases, intrusion detection, malware, application state, alerts,compute inventory, network sessions, network traffic, performance,audits, updates, vulnerabilities, etc. Data models and their objects canbe designed by knowledge managers in an organization, and they canenable downstream users to quickly focus on a specific set of data. Auser iteratively applies a model development tool (not shown in FIG. 8A)to prepare a query that defines a subset of events and assigns an objectname to that subset. A child subset is created by further limiting aquery that generated a parent subset.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No.9,128,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”,issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATIONOF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, eachof which is hereby incorporated by reference in its entirety for allpurposes.

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In some embodiments, the data intake and query system 108 provides theuser with the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes. Datavisualizations also can be generated in a variety of formats, byreference to the data model. Reports, data visualizations, and datamodel objects can be saved and associated with the data model for futureuse. The data model object may be used to perform searches of otherdata. As described in greater detail in U.S. patent application Ser. No.15/665,159, entitled “MULTI-LAYER PARTITION ALLOCATION FOR QUERYEXECUTION”, filed on Jul. 31, 2017, and which is hereby incorporated byreference in its entirety for all purposes, various interfaces can beused to generate and display data models.

3.13. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally-processed data “on thefly” at search time using a late-binding schema, instead of storingpre-specified portions of the data in a database at ingestion time. Thisflexibility enables a user to see valuable insights, correlate data, andperform subsequent queries to examine interesting aspects of the datathat may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, the data intake and query system also employs anumber of unique acceleration techniques that have been developed tospeed up analysis operations performed at search time. These techniquesinclude: (1) performing search operations in parallel across multipleindexers; (2) using a keyword index; (3) using a high performanceanalytics store; and (4) accelerating the process of generating reports.These novel techniques are described in more detail below. Althoughdescribed as being performed by an indexer, it will be understood thatvarious components can be used to perform similar functionality. Forexample, nodes can perform any one or any combination of the searchfunctions described herein. In some cases, the nodes perform the searchfunctions based on instructions received from a query coordinator.

3.13.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 9 is an example search query receivedfrom a client and executed by search peers, in accordance with exampleembodiments. FIG. 9 illustrates how a search query 902 received from aclient at a search head 210 can split into two phases, including: (1)subtasks 904 (e.g., data retrieval or simple filtering) that may beperformed in parallel by indexers 206 for execution, and (2) a searchresults aggregation operation 906 to be executed by the search head whenthe results are ultimately collected from the indexers.

During operation, upon receiving search query 902, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 902 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 904, and then distributes searchquery 904 to distributed indexers, which are also referred to as “searchpeers” or “peer indexers.” Note that search queries may generallyspecify search criteria or operations to be performed on events thatmeet the search criteria. Search queries may also specify field names,as well as search criteria for the values in the fields or operations tobe performed on the values in the fields.

Moreover, the search head may distribute the full search query to thesearch peers as illustrated in FIG. 6A, or may alternatively distributea modified version (e.g., a more restricted version) of the search queryto the search peers. In this example, the indexers are responsible forproducing the results and sending them to the search head. After theindexers return the results to the search head, the search headaggregates the received results 906 to form a single search result set.By executing the query in this manner, the system effectivelydistributes the computational operations across the indexers whileminimizing data transfers.

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, 18-49, some functionality of the searchhead or indexers can be handled by different components of the system orremoved altogether. For example, in some cases, the data is stored inone or more dataset sources, such as, but not limited to an indexer (ordata store controlled by an indexer), common storage, external datasource, ingested data buffer, query acceleration data store, etc. Inaddition, in some cases a query coordinator can aggregate results frommultiple indexers or nodes, perform an aggregation operation 906,determine what, if any, portion of the operations of the search queryare to be performed locally the query coordinator, modify or translate asearch query for an indexer or other dataset source, distribute thequery to indexers, peers, or nodes, etc.

3.13.2. Keyword Index

As described above with reference to the flow charts in FIG. 5A, FIG.5B, and FIG. 6A, data intake and query system 108 can construct andmaintain one or more keyword indices to quickly identify eventscontaining specific keywords. This technique can greatly speed up theprocessing of queries involving specific keywords. As mentioned above,to build a keyword index, an indexer first identifies a set of keywords.Then, the indexer includes the identified keywords in an index, whichassociates each stored keyword with references to events containing thatkeyword, or to locations within events where that keyword is located.When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword. In some embodiments, a node or other components of the systemthat performs search operations can use the keyword index to identifyevents, etc.

3.13.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the events and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No.9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STOREWITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”,issued on 8 Sep. 2015, and U.S. patent application Ser. No. 14/815,973,entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OFSEARCHABLE EVENTS”, filed on 1 Aug. 2015, each of which is herebyincorporated by reference in its entirety for all purposes.

To speed up certain types of queries, e.g., frequently encounteredqueries or computationally intensive queries, some embodiments of system108 create a high performance analytics store, which is referred to as a“summarization table,” (also referred to as a “lexicon” or “invertedindex”) that contains entries for specific field-value pairs. Each ofthese entries keeps track of instances of a specific value in a specificfield in the event data and includes references to events containing thespecific value in the specific field. For example, an example entry inan inverted index can keep track of occurrences of the value “94107” ina “ZIP code” field of a set of events and the entry includes referencesto all of the events that contain the value “94107” in the ZIP codefield. Creating the inverted index data structure avoids needing toincur the computational overhead each time a statistical query needs tobe run on a frequently encountered field-value pair. In order toexpedite queries, in most embodiments, the search engine will employ theinverted index separate from the raw record data store to generateresponses to the received queries.

Note that the term “summarization table” or “inverted index” as usedherein is a data structure that may be generated by an indexer thatincludes at least field names and field values that have been extractedand/or indexed from event records. An inverted index may also includereference values that point to the location(s) in the field searchabledata store where the event records that include the field may be found.Also, an inverted index may be stored using well-known compressiontechniques to reduce its storage size.

Further, note that the term “reference value” (also referred to as a“posting value”) as used herein is a value that references the locationof a source record in the field searchable data store. In someembodiments, the reference value may include additional informationabout each record, such as timestamps, record size, meta-data, or thelike. Each reference value may be a unique identifier which may be usedto access the event data directly in the field searchable data store. Insome embodiments, the reference values may be ordered based on eachevent record's timestamp. For example, if numbers are used asidentifiers, they may be sorted so event records having a latertimestamp always have a lower valued identifier than event records withan earlier timestamp, or vice-versa. Reference values are often includedin inverted indexes for retrieving and/or identifying event records.

In one or more embodiments, an inverted index is generated in responseto a user-initiated collection query. The term “collection query” asused herein refers to queries that include commands that generatesummarization information and inverted indexes (or summarization tables)from event records stored in the field searchable data store.

Note that a collection query is a special type of query that can beuser-generated and is used to create an inverted index. A collectionquery is not the same as a query that is used to call up or invoke apre-existing inverted index. In one or more embodiment, a query cancomprise an initial step that calls up a pre-generated inverted index onwhich further filtering and processing can be performed. For example,referring back to FIG. 13, a set of events generated at block 1320 byeither using a “collection” query to create a new inverted index or bycalling up a pre-generated inverted index. A query with severalpipelined steps will start with a pre-generated index to accelerate thequery.

FIG. 7C illustrates the manner in which an inverted index is created andused in accordance with the disclosed embodiments. As shown in FIG. 7C,an inverted index 722 can be created in response to a user-initiatedcollection query using the event data 723 stored in the raw record datastore. For example, a non-limiting example of a collection query mayinclude “collect clientip=127.0.0.1” which may result in an invertedindex 722 being generated from the event data 723 as shown in FIG. 7C.Each entry in inverted index 722 includes an event reference value thatreferences the location of a source record in the field searchable datastore. The reference value may be used to access the original eventrecord directly from the field searchable data store.

In one or more embodiments, if one or more of the queries is acollection query, the responsive indexers may generate summarizationinformation based on the fields of the event records located in thefield searchable data store. In at least one of the various embodiments,one or more of the fields used in the summarization information may belisted in the collection query and/or they may be determined based onterms included in the collection query. For example, a collection querymay include an explicit list of fields to summarize. Or, in at least oneof the various embodiments, a collection query may include terms orexpressions that explicitly define the fields, e.g., using regex rules.In FIG. 7C, prior to running the collection query that generates theinverted index 722, the field name “clientip” may need to be defined ina configuration file by specifying the “access_combined” source type anda regular expression rule to parse out the client IP address.Alternatively, the collection query may contain an explicit definitionfor the field name “clientip” which may obviate the need to referencethe configuration file at search time.

In one or more embodiments, collection queries may be saved andscheduled to run periodically. These scheduled collection queries mayperiodically update the summarization information corresponding to thequery. For example, if the collection query that generates invertedindex 722 is scheduled to run periodically, one or more indexers wouldperiodically search through the relevant buckets to update invertedindex 722 with event data for any new events with the “clientip” valueof “127.0.0.1.”

In some embodiments, the inverted indexes that include fields, values,and reference value (e.g., inverted index 722) for event records may beincluded in the summarization information provided to the user. In otherembodiments, a user may not be interested in specific fields and valuescontained in the inverted index, but may need to perform a statisticalquery on the data in the inverted index. For example, referencing theexample of FIG. 7C rather than viewing the fields within summarizationtable 722, a user may want to generate a count of all client requestsfrom IP address “127.0.0.1.” In this case, the search engine wouldsimply return a result of “4” rather than including details about theinverted index 722 in the information provided to the user.

The pipelined search language, e.g., SPL of the SPLUNK® ENTERPRISEsystem can be used to pipe the contents of an inverted index to astatistical query using the “stats” command for example. A “stats” queryrefers to queries that generate result sets that may produce aggregateand statistical results from event records, e.g., average, mean, max,min, rms, etc. Where sufficient information is available in an invertedindex, a “stats” query may generate their result sets rapidly from thesummarization information available in the inverted index rather thandirectly scanning event records. For example, the contents of invertedindex 722 can be pipelined to a stats query, e.g., a “count” functionthat counts the number of entries in the inverted index and returns avalue of “4.” In this way, inverted indexes may enable various statsqueries to be performed absent scanning or search the event records.Accordingly, this optimization technique enables the system to quicklyprocess queries that seek to determine how many events have a particularvalue for a particular field. To this end, the system can examine theentry in the inverted index to count instances of the specific value inthe field without having to go through the individual events or performdata extractions at search time.

In some embodiments, the system maintains a separate inverted index foreach of the above-described time-specific buckets that stores events fora specific time range. A bucket-specific inverted index includes entriesfor specific field-value combinations that occur in events in thespecific bucket. Alternatively, the system can maintain a separateinverted index for each indexer. The indexer-specific inverted indexincludes entries for the events in a data store that are managed by thespecific indexer. Indexer-specific inverted indexes may also bebucket-specific. In at least one or more embodiments, if one or more ofthe queries is a stats query, each indexer may generate a partial resultset from previously generated summarization information. The partialresult sets may be returned to the search head that received the queryand combined into a single result set for the query

As mentioned above, the inverted index can be populated by running aperiodic query that scans a set of events to find instances of aspecific field-value combination, or alternatively instances of allfield-value combinations for a specific field. A periodic query can beinitiated by a user, or can be scheduled to occur automatically atspecific time intervals. A periodic query can also be automaticallylaunched in response to a query that asks for a specific field-valuecombination. In some embodiments, if summarization information is absentfrom an indexer that includes responsive event records, further actionsmay be taken, such as, the summarization information may generated onthe fly, warnings may be provided the user, the collection queryoperation may be halted, the absence of summarization information may beignored, or the like, or combination thereof.

In one or more embodiments, an inverted index may be set up to updatecontinually. For example, the query may ask for the inverted index toupdate its result periodically, e.g., every hour. In such instances, theinverted index may be a dynamic data structure that is regularly updatedto include information regarding incoming events.

In some cases, e.g., where a query is executed before an inverted indexupdates, when the inverted index may not cover all of the events thatare relevant to a query, the system can use the inverted index to obtainpartial results for the events that are covered by inverted index, butmay also have to search through other events that are not covered by theinverted index to produce additional results on the fly. In other words,an indexer would need to search through event data on the data store tosupplement the partial results. These additional results can then becombined with the partial results to produce a final set of results forthe query. Note that in typical instances where an inverted index is notcompletely up to date, the number of events that an indexer would needto search through to supplement the results from the inverted indexwould be relatively small. In other words, the search to get the mostrecent results can be quick and efficient because only a small number ofevent records will be searched through to supplement the informationfrom the inverted index. The inverted index and associated techniquesare described in more detail in U.S. Pat. No. 8,682,925, entitled“DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014,U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCEANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO ANEVENT QUERY”, filed on 31 Jan. 2014, and U.S. patent application Ser.No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROL DEVICE”, filed on21 Feb. 2014, each of which is hereby incorporated by reference in itsentirety. In some cases, the inverted indexes can be made available, aspart of a common storage, to nodes or other components of the systemthat perform search operations.

3.13.4. Extracting Event Data Using Posting

In one or more embodiments, if the system needs to process all eventsthat have a specific field-value combination, the system can use thereferences in the inverted index entry to directly access the events toextract further information without having to search all of the eventsto find the specific field-value combination at search time. In otherwords, the system can use the reference values to locate the associatedevent data in the field searchable data store and extract furtherinformation from those events, e.g., extract further field values fromthe events for purposes of filtering or processing or both.

The information extracted from the event data using the reference valuescan be directed for further filtering or processing in a query using thepipeline search language. The pipelined search language will, in oneembodiment, include syntax that can direct the initial filtering step ina query to an inverted index. In one embodiment, a user would includesyntax in the query that explicitly directs the initial searching orfiltering step to the inverted index.

Referencing the example in FIG. 7B, if the user determines that sheneeds the user id fields associated with the client requests from IPaddress “127.0.0.1,” instead of incurring the computational overhead ofperforming a brand new search or re-generating the inverted index withan additional field, the user can generate a query that explicitlydirects or pipes the contents of the already generated inverted index1502 to another filtering step requesting the user ids for the entriesin inverted index 1502 where the server response time is greater than“0.0900” microseconds. The search engine would use the reference valuesstored in inverted index 722 to retrieve the event data from the fieldsearchable data store, filter the results based on the “response time”field values and, further, extract the user id field from the resultingevent data to return to the user. In the present instance, the user ids“frank” and “carlos” would be returned to the user from the generatedresults table 722.

In one embodiment, the same methodology can be used to pipe the contentsof the inverted index to a processing step. In other words, the user isable to use the inverted index to efficiently and quickly performaggregate functions on field values that were not part of the initiallygenerated inverted index. For example, a user may want to determine anaverage object size (size of the requested gif) requested by clientsfrom IP address “127.0.0.1.” In this case, the search engine would againuse the reference values stored in inverted index 722 to retrieve theevent data from the field searchable data store and, further, extractthe object size field values from the associated events 731, 732, 733and 734. Once, the corresponding object sizes have been extracted (i.e.2326, 2900, 2920, and 5000), the average can be computed and returned tothe user.

In one embodiment, instead of explicitly invoking the inverted index ina user-generated query, e.g., by the use of special commands or syntax,the SPLUNK® ENTERPRISE system can be configured to automaticallydetermine if any prior-generated inverted index can be used to expeditea user query. For example, the user's query may request the averageobject size (size of the requested gif) requested by clients from IPaddress “127.0.0.1.” without any reference to or use of inverted index722. The search engine, in this case, would automatically determine thatan inverted index 722 already exists in the system that could expeditethis query. In one embodiment, prior to running any search comprising afield-value pair, for example, a search engine may search though all theexisting inverted indexes to determine if a pre-generated inverted indexcould be used to expedite the search comprising the field-value pair.Accordingly, the search engine would automatically use the pre-generatedinverted index, e.g., index 722 to generate the results without anyuser-involvement that directs the use of the index.

Using the reference values in an inverted index to be able to directlyaccess the event data in the field searchable data store and extractfurther information from the associated event data for further filteringand processing is highly advantageous because it avoids incurring thecomputation overhead of regenerating the inverted index with additionalfields or performing a new search.

The data intake and query system includes one or more forwarders thatreceive raw machine data from a variety of input data sources, and oneor more indexers that process and store the data in one or more datastores. By distributing events among the indexers and data stores, theindexers can analyze events for a query in parallel. In one or moreembodiments, a multiple indexer implementation of the search systemwould maintain a separate and respective inverted index for each of theabove-described time-specific buckets that stores events for a specifictime range. A bucket-specific inverted index includes entries forspecific field-value combinations that occur in events in the specificbucket. As explained above, a search head would be able to correlate andsynthesize data from across the various buckets and indexers.

This feature advantageously expedites searches because instead ofperforming a computationally intensive search in a centrally locatedinverted index that catalogues all the relevant events, an indexer isable to directly search an inverted index stored in a bucket associatedwith the time-range specified in the query. This allows the search to beperformed in parallel across the various indexers. Further, if the queryrequests further filtering or processing to be conducted on the eventdata referenced by the locally stored bucket-specific inverted index,the indexer is able to simply access the event records stored in theassociated bucket for further filtering and processing instead ofneeding to access a central repository of event records, which woulddramatically add to the computational overhead.

In one embodiment, there may be multiple buckets associated with thetime-range specified in a query. If the query is directed to an invertedindex, or if the search engine automatically determines that using aninverted index would expedite the processing of the query, the indexerswill search through each of the inverted indexes associated with thebuckets for the specified time-range. This feature allows the HighPerformance Analytics Store to be scaled easily.

In certain instances, where a query is executed before a bucket-specificinverted index updates, when the bucket-specific inverted index may notcover all of the events that are relevant to a query, the system can usethe bucket-specific inverted index to obtain partial results for theevents that are covered by bucket-specific inverted index, but may alsohave to search through the event data in the bucket associated with thebucket-specific inverted index to produce additional results on the fly.In other words, an indexer would need to search through event datastored in the bucket (that was not yet processed by the indexer for thecorresponding inverted index) to supplement the partial results from thebucket-specific inverted index.

FIG. 7D presents a flowchart illustrating how an inverted index in apipelined search query can be used to determine a set of event data thatcan be further limited by filtering or processing in accordance with thedisclosed embodiments.

At block 742, a query is received by a data intake and query system. Insome embodiments, the query can be receive as a user generated queryentered into search bar of a graphical user search interface. The searchinterface also includes a time range control element that enablesspecification of a time range for the query.

At block 744, an inverted index is retrieved. Note, that the invertedindex can be retrieved in response to an explicit user search commandinputted as part of the user generated query. Alternatively, the searchengine can be configured to automatically use an inverted index if itdetermines that using the inverted index would expedite the servicing ofthe user generated query. Each of the entries in an inverted index keepstrack of instances of a specific value in a specific field in the eventdata and includes references to events containing the specific value inthe specific field. In order to expedite queries, in most embodiments,the search engine will employ the inverted index separate from the rawrecord data store to generate responses to the received queries.

At block 746, the query engine determines if the query contains furtherfiltering and processing steps. If the query contains no furthercommands, then, in one embodiment, summarization information can beprovided to the user at block 754.

If, however, the query does contain further filtering and processingcommands, then at block 750, the query engine determines if the commandsrelate to further filtering or processing of the data extracted as partof the inverted index or whether the commands are directed to using theinverted index as an initial filtering step to further filter andprocess event data referenced by the entries in the inverted index. Ifthe query can be completed using data already in the generated invertedindex, then the further filtering or processing steps, e.g., a “count”number of records function, “average” number of records per hour etc.are performed and the results are provided to the user at block 752.

If, however, the query references fields that are not extracted in theinverted index, then the indexers will access event data pointed to bythe reference values in the inverted index to retrieve any furtherinformation required at block 756. Subsequently, any further filteringor processing steps are performed on the fields extracted directly fromthe event data and the results are provided to the user at step 758.

As described throughout, it will be understood that although describedas being performed by an indexer, these functions can be performed byanother component of the system, such as a query coordinator or node.For example, nodes can use inverted indexes to identify relevant data,etc. The inverted indexes can be stored with buckets in a commonstorage, etc.

3.13.5. Accelerating Report Generation

In some embodiments, a data server system such as the data intake andquery system can accelerate the process of periodically generatingupdated reports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on theseadditional events. Then, the results returned by this query on theadditional events, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer events needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety for all purposes.

3.14. Security Features

The data intake and query system provides various schemas, dashboards,and visualizations that simplify developers' tasks to createapplications with additional capabilities. One such application is thean enterprise security application, such as SPLUNK® ENTERPRISE SECURITY,which performs monitoring and alerting operations and includes analyticsto facilitate identifying both known and unknown security threats basedon large volumes of data stored by the data intake and query system. Theenterprise security application provides the security practitioner withvisibility into security-relevant threats found in the enterpriseinfrastructure by capturing, monitoring, and reporting on data fromenterprise security devices, systems, and applications. Through the useof the data intake and query system searching and reportingcapabilities, the enterprise security application provides a top-downand bottom-up view of an organization's security posture.

The enterprise security application leverages the data intake and querysystem search-time normalization techniques, saved searches, andcorrelation searches to provide visibility into security-relevantthreats and activity and generate notable events for tracking. Theenterprise security application enables the security practitioner toinvestigate and explore the data to find new or unknown threats that donot follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemslack the infrastructure to effectively store and analyze large volumesof security-related data. Traditional SIEM systems typically use fixedschemas to extract data from pre-defined security-related fields at dataingestion time and store the extracted data in a relational database.This traditional data extraction process (and associated reduction indata size) that occurs at data ingestion time inevitably hampers futureincident investigations that may need original data to determine theroot cause of a security issue, or to detect the onset of an impendingsecurity threat.

In contrast, the enterprise security application system stores largevolumes of minimally-processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the enterprise security application provides pre-specified schemas forextracting relevant values from the different types of security-relatedevents and enables a user to define such schemas.

The enterprise security application can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTIONOF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issuedon 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OFSECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTEREDDOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled“SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTIONUSING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No.9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAMEREGISTRATIONS”, issued on 30 Aug. 2016, each of which is herebyincorporated by reference in its entirety for all purposes.Security-related information can also include malware infection data andsystem configuration information, as well as access control information,such as login/logout information and access failure notifications. Thesecurity-related information can originate from various sources within adata center, such as hosts, virtual machines, storage devices andsensors. The security-related information can also originate fromvarious sources in a network, such as routers, switches, email servers,proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the enterprise security application facilitatesdetecting “notable events” that are likely to indicate a securitythreat. A notable event represents one or more anomalous incidents, theoccurrence of which can be identified based on one or more events (e.g.,time stamped portions of raw machine data) fulfilling pre-specifiedand/or dynamically-determined (e.g., based on machine-learning) criteriadefined for that notable event. Examples of notable events include therepeated occurrence of an abnormal spike in network usage over a periodof time, a single occurrence of unauthorized access to system, a hostcommunicating with a server on a known threat list, and the like. Thesenotable events can be detected in a number of ways, such as: (1) a usercan notice a correlation in events and can manually identify that acorresponding group of one or more events amounts to a notable event; or(2) a user can define a “correlation search” specifying criteria for anotable event, and every time one or more events satisfy the criteria,the application can indicate that the one or more events correspond to anotable event; and the like. A user can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

As described in greater detail U.S. patent application Ser. No.15/665,159, entitled “MULTI-LAYER PARTITION ALLOCATION FOR QUERYEXECUTION”, filed on Jul. 31, 2017, and which is hereby incorporated byreference in its entirety for all purposes, various visualizations canbe included to aid in discovering security threats, to monitor virtualmachines, to monitor IT environments, etc.

4.0. Data Intake and Fabric System Architecture

The capabilities of a data intake and query system are typically limitedto resources contained within that system. For example, the data intakeand query system has search and analytics capabilities that are limitedin scope to the indexers responsible for storing and searching a subsetof events contained in their corresponding internal data stores.

Even if a data intake and query system has access to external datastores that may include data relevant to a query, the data intake andquery system typically has limited capabilities to process thecombination of partial search results from the indexers and externaldata sources to produce comprehensive search results. In particular, thesearch head of a data intake and query system may retrieve partialsearch results from external data systems over a network. The searchhead may also retrieve partial results from its indexers, and combinethose partial search results with the partial results of the externaldata sources to produce final results for a query.

For example, the search head can implement map-reduce techniques, whereeach data source returns partial search results and the search head cancombine the partial search results to produce the final results of aquery. However, obtaining results in this manner from distributed datasystems including internal data stores and external data stores haslimited value because the search head can act as a bottleneck forprocessing complex search queries on distributed data systems. Thebottleneck effect at the search head worsens as the number ofdistributed data systems increases. Furthermore, even without processingqueries on distributed data systems, the search head 210 and theindexers 206 can act as bottlenecks due to the number of queriesreceived by the data intake and query system 108 and the amount ofprocessing done by the indexers during data ingestion, indexing, andsearch.

Embodiments of the disclosed data fabric service (DFS) system 1001overcome the aforementioned drawbacks by expanding on the capabilitiesof a data intake and query system to enable application of a queryacross distributed data systems, which may also be referred to asdataset sources, including internal data stores coupled to indexers(illustrated in FIG. 10), external data stores coupled to the dataintake and query system over a network (illustrated in FIGS. 10, 17,18), common storage (illustrated in FIGS. 17, 18), query accelerationdata stores (e.g., query acceleration data store 1008 illustrated inFIGS. 10, 17, 18), ingested data buffers (illustrated in FIG. 18) thatinclude ingested streaming data. Moreover, the disclosed embodiments arescalable to accommodate application of a query on a growing number ofdiverse data systems. Additional embodiments are disclosed in U.S.patent application Ser. No. 15/665,159, entitled “MULTI-LAYER PARTITIONALLOCATION FOR QUERY EXECUTION”, filed on Jul. 31, 2017, and which ishereby incorporated by reference in its entirety for all purposes.

In certain embodiments, the disclosed DFS system extends thecapabilities of the data intake and query system and mitigates thebottleneck effect at the search head by including one or more querycoordinators communicatively coupled to worker nodes distributed in abig data ecosystem. In some embodiments, the worker nodes can becommunicatively coupled to the various dataset sources (e.g., indexers,common storage, external data systems that contain external data stores,ingested data buffers, query acceleration data stores, etc.)

The data intake and query system can receive a query input by a user ata client device via a search head. The search head can coordinate with asearch process master and/or one or more query coordinators (the searchprocess master and query coordinators can collectively referred to as asearch process service) to execute a search scheme applied to one ormore dataset sources (e.g., indexers, common storage, ingested databuffer, query acceleration data store, external data stores, etc.). Theworker nodes can collect, process, and aggregate the partial resultsfrom the dataset sources, and transfer the aggregate results to a querycoordinator. In some embodiments, the query coordinator can operate onthe aggregate results, and send finalized results to the search head,which can render the results of the query on a display device.

Hence, the search head in conjunction with the search process master andquery coordinator(s) can apply a query to any one or more of thedistributed dataset sources. The worker nodes can act in accordance withthe instructions received by a query coordinator to obtain relevantdatasets from the different dataset sources, process the datasets,aggregate the partial results of processing the different datasets, andcommunicate the aggregated results to the query coordinator, orelsewhere. In other words, the search head of the data intake and querysystem can offload at least some query processing to the querycoordinator and worker nodes, to both obtain the datasets from thedataset sources and aggregate the results of processing the differentdatasets. This system is scalable to accommodate any number of workernodes communicatively coupled to any number and types of data sources.

Thus, embodiments of the DFS system can extend the capabilities of adata intake and query system by leveraging computing assets fromanywhere in a big data ecosystem to collectively execute queries ondiverse data systems regardless of whether data stores are internal ofthe data intake and query system and/or external data stores that arecommunicatively coupled to the data intake and query system over anetwork.

FIG. 10 is a system diagram illustrating an environment 1000 foringesting and indexing data, and performing queries on one or moredatasets from one or more dataset sources. In the illustratedembodiment, the environment 1000 includes data sources 201, clientdevices 404, described in greater detail above with reference to FIG. 4,and external data sources 1018 communicatively coupled to a data intakeand query system 1001. The external data sources 1018 can be similar tothe external data systems 12-1, 12-2 described above with reference toFIG. 1A or the external data sources described above with reference toFIG. 4.

In the illustrated embodiment, the data intake and query system 1001includes any combination of forwarders 204, indexers 206, data stores208, and a search head 210, as discussed in greater detail above withreference to FIGS. 2-4. For example, the forwarders 204 can forward datafrom the data sources 202 to the indexers 206, the indexers can 206ingest, parse, index, and store the data in the data stores 208, and thesearch head 210 can receive queries from, and provide the results of thequeries to, client devices 404 on behalf of the system 1001.

In addition to forwarders 204, indexers 206, data stores 208, and thesearch head 210, the system 1001 further includes a search processmaster 1002 (in some embodiments also referred to as DFS master), one ormore query coordinators 1004 (in some embodiments also referred to assearch service providers), worker nodes 1006, and a query accelerationdata store 1008. In some embodiments, a workload advisor 1010, workloadcatalog 1012, node monitor 1014, and dataset compensation module 1016can be included in the search process master 1002. However, it will beunderstood that any one or any combination of the workload advisor 1010,workload catalog 1012, node monitor 1014, and dataset compensationmodule 1016 can be included elsewhere in the system 1001, such as in asa separate device or as part of a query coordinator 1004.

As will be described in greater detail below, the functionality of thesearch head 210 and the indexers 206 in the illustrated embodiment ofFIG. 10 can differ in some respects from the functionality describedpreviously with respect to other embodiments. For example, in theillustrated embodiment of FIG. 10, the search head 210 can perform someprocessing on the query and then communicate the query to the searchprocess master 1002 and coordinator(s) 1004 for further processing andexecution. For example, the search head 210 can authenticate the clientdevice or user that sent the query, check the syntax and/or semantics ofthe query, or otherwise determine that the search request is valid. Insome cases, a daemon running on the search head 210 can receive a query.In response, the search head 210 can spawn a search process to furtherhandle the query, including communicating the query to the searchprocess master 1002 or query coordinator 1004. Upon completion of thequery, the search head 210 can receive the results of the query from thesearch process master 1002 or query coordinator 1004 and serve theresults to the client device 404. In such embodiments, the search head210 may not perform any additional processing on the results receivedfrom the search process master 1002 or query coordinator 1004. In somecases, upon receiving and communicating the results, the search head 210can terminate the search process.

In addition, the indexers 206 in the illustrated embodiment of FIG. 10can receive the relevant subqueries from the query coordinator 1004rather than the search head 210, search the corresponding data stores208 for relevant events, and provide their individual results of thesearch to the worker nodes 1006 instead of the search head 210 forfurther processing. As described previously, the indexers 206 cananalyze events for a query in parallel. For example, each indexer 206can search its corresponding data stores 208 in parallel and communicateits partial results to the worker nodes 1006.

The search head 210, search process master 1002, and query coordinator1004 can be implemented using separate computer systems, processors, orvirtual machines, or may alternatively comprise separate processesexecuting on one or more computer systems, processors, or virtualmachines. In some embodiments, running the search head 210, searchprocess master 1002, and/or query coordinator 1004 on the same machinecan increase performance of the system 1001 by reducing communicationsover networks. In either case, the search process master 1002 and querycoordinator 1004 can be communicatively coupled to the search head 210.

The search process master 1002 and query coordinator 1004 can be used toreduce the processing demands on the search head 210. Specifically, thesearch process master 1002 and coordinator 1004 can perform some of thepreliminary query processing to reduce the amount of processing done bythe search head 210 upon receipt of a query. In addition, the searchprocess master 1002 and coordinator 1004 can perform some of theprocessing on the results of the query to reduce the amount ofprocessing done by the search head 210 prior to communicating theresults to a client device. For example, upon receipt of a query, thesearch head 210 can determine that the query can be processed by thesearch process master 1002. In turn, the search process master 1002 canidentify a query coordinator 1004 that can process the query. In somecases, if there is not a query coordinator 1004 that can handle theincoming query, the search process master 1002 can spawn an additionalquery coordinator 1004 to handle the query.

The query coordinator(s) 1004 can coordinate the various tasks toexecute queries assigned to them and return the results to the searchhead 210. For example, as will be described in greater detail below, thequery coordinator 1004 can determine the amount of resources availablefor a query, allocate resources for the query, determine how the queryis to be broken up between dataset sources, generate commands for thedataset sources to execute, determine what tasks are to be handled bythe worker nodes 1006, spawn the worker nodes 1006 for the differenttasks, instruct different worker nodes 1006 to perform the differenttasks and where to route the results of each task, monitor the workernodes 1006 during the query, control the flow of data between the workernodes 1006, process the aggregate results from the worker nodes 1006,and send the finalized results to the search head 210 or to anotherdataset destination. In addition, the query coordinators 1004 caninclude providing data isolation across different searches based onrole/access control, as well as fault tolerance (e.g., localized to asearch head). For example, if a search operation fails, then its spawnedquery coordinator 1004 may fail but other query coordinators 1004 forother queries can continue to operate. In addition, queries that are tobe isolated from one another can use different query coordinators 1004.

The worker nodes 1006 can perform the various tasks assigned to them bya query coordinator 1004. For example, the worker nodes 1006 can intakedata from the various dataset sources, process the data according to thequery, collect results from the processing, combine results from variousoperations, route the results to various destinations, etc. In certaincases, the worker nodes 1006 and indexers 206 can be implemented usingseparate computer systems, processors, or virtual machines, or mayalternatively comprise separate processes executing on one or morecomputer systems, processors, or virtual machines.

The query acceleration data store 1008 can be used to store datasets foraccelerated access. In some cases, the worker nodes 1006 can obtain datafrom the indexers 206, external data sources 1018, or other location(e.g., common storage, ingested data buffer, etc.) and store the data inthe query acceleration data store 1008. In such embodiments, when aquery is received that relates to the data stored in the queryacceleration data store 1008, the worker nodes 1006 can access the datain the query acceleration data store 1008 and process the data accordingto the query. Furthermore, if the query also includes a request fordatasets that are not in the query acceleration data store 1008, theworker nodes 1006 can begin working on the dataset obtained from thequery acceleration data store 1008, while also obtaining the otherdataset(s) from the other dataset source(s). In this way, a clientdevice 414 a-404 n can rapidly receive a response to a provided query,while the worker nodes 1006 obtain datasets from the other datasetsources.

The query acceleration data store 1008 can be, for example, adistributed in-memory database system, storage subsystem, and so on,which can maintain (e.g., store) datasets in both low-latency memory(e.g., random access memory, such as volatile or non-volatile memory)and longer-latency memory (e.g., solid state storage, disk drives, andso on). To increase efficiency and response times, the accelerated dataset 1008 can maintain particular datasets in the low-latency memory, andother datasets in the longer-latency memory. For example, the datasetscan be stored in-memory (non-limiting examples: RAM or volatile memory)with disk spillover (non-limiting examples: hard disks, disk drive,non-volatile memory, etc.). In this way, the query acceleration datastore 1008 can be used to serve interactive or iterative searches. Insome cases, datasets which are determined to be frequently accessed by auser can be stored in the lower-latency memory. Similarly, datasets ofless than a threshold size can be stored in the lower-latency memory.

As will be described below, a user can indicate in a query thatparticular datasets are to be stored in the query acceleration datastore 1008. The query can then indicate operations to be performed onthe particular datasets. For subsequent queries directed to theparticular datasets (e.g., queries that indicate other operations), theworker nodes 1006 can obtain information directly from the queryacceleration data store 1008. Additionally, since the query accelerationdata store 1008 can be utilized to service requests from differentclients 404 a-404 n, the query acceleration data store 1008 canimplement access controls (e.g., an access control list) with respect tothe stored datasets. In this way, the stored datasets can optionally beaccessible only to users associated with requests for the datasets.Optionally, a user who provides a query can indicate that one or moreother users are authorized to access particular requested datasets. Inthis way, the other users can utilize the stored datasets, thus reducinglatency associated with their queries.

In certain embodiments, the worker nodes 1006 can store data from anydataset source, including data from a dataset source that has not beentransformed by the nodes 1006, processed data (e.g., data that has beentransformed by the nodes 1006), partial results, or aggregated resultsfrom a query in the query acceleration data store 1008. In suchembodiments, the results stored in the query acceleration data store1008 can be served at a later time to the search head 210, combined withadditional results obtained from a later query, transformed or furtherprocessed by the worker nodes 1006, etc.

It will be understood that the system 1001 can include fewer or morecomponents as desired. For example, in some embodiments, the system 1001does not include a search head 210. In such embodiments, the searchprocess master 1002 can receive query requests from clients 404 andreturn results of the query to the client devices 404. Further, it willbe understood that in some embodiments, the functionality describedherein for one component can be performed by another component. Forexample, although the workload advisor 1010 and dataset compensationmodule 1016 are described as being implemented in the search processmaster 1002, it will be understood that these components and theirfunctionality can be implemented in the query coordinator 1004.Similarly, as will be described in greater detail below, in someembodiments, the nodes 1006 can be used to index data and store it inone or more data stores, such as the common storage or ingested databuffer, described in greater detail below.

4.1. Worker Nodes

FIG. 11 is a block diagram illustrating an embodiment of multiplemachines 1102, each having multiple nodes 1006-1, 1006-n (individuallyand collectively referred to as node 1006 or nodes 1006) residingthereon. The worker nodes 1006 across the various machines 1102 can becommunicatively coupled to each other, to the various components of thesystem 1001, such as the indexers 206, query coordinator 1004, searchhead 210, common storage, ingested data buffer, etc., and to theexternal data sources 1018.

The machines 1102 can be implemented using multi-core servers orcomputing systems and can include an operating system layer 1104 withwhich the nodes 1006 interact. For example, in some embodiments, eachmachine 1102 can include 32, 48, 64, or more processor cores, multipleterabytes of memory, etc.

In the illustrated embodiment, each node 1006 includes four processors1106, memory 1108, a monitoring module 1110, and aserialization/deserialization module 1112. It will be understood thateach node 1006 can include fewer or more components as desired.Furthermore, it will be understood that the nodes 1006 can includedifferent components and resources from each other. For example node1006-1 can include fewer or more processors 1106 or memory 1108 than thenode 1006-n.

The processors 1106 and memory 1108 can be used by the nodes 1006 toperform the tasks assigned to it by the query coordinator 1004 and cancorrespond to a subset of the memory and processors of the machine 1102.The serialization/deserialization module 1112 can be used toserialize/deserialize data for communication between components of thesystem 1001, as will be described in greater detail below.

The monitoring module 1110 can be used to monitor the state andutilization rate of the node 1006 or processors 1106 and report theinformation to the search process master 1002 or query coordinator 1004.For example, the monitoring module 1110 can indicate the number ofprocessors in use by the node 1006, the utilization rate of eachprocessor, whether a processor is unavailable or not functioning, theamount of memory used by the processors 1106 or node 1006, etc.

In addition, each worker node 1006 can include one or more softwarecomponents or modules (“modules”) operable to carry out the functions ofthe system 1001 by communicating with the query coordinator 1004, theindexers 206, and the dataset sources. The modules can run on aprogramming interface of the worker nodes 1006. An example of such aninterface is APACHE SPARK, which is an open source computing frameworkthat can be used to execute the worker nodes 1006 with implicitparallelism and fault-tolerance.

In particular, SPARK includes an application programming interface (API)centered on a data structure called a resilient distributed dataset(RDD), which is a read-only multiset of data items distributed over acluster of machines (e.g., the devices running the worker nodes 1006).The RDDs function as a working set for distributed programs that offer aform of distributed shared memory.

Based on instructions received from the query coordinator 1004, theworker nodes 1006 can collect and process data or partial search resultsof a distributed network of data storage systems, and provide aggregatedpartial search results or finalized search results to the querycoordinator 1004 or other destination. Accordingly, the querycoordinator 1004 can act as a manager of the worker nodes 1006,including their distributed data storage systems, to extract, collect,and store partial search results via their modules running on acomputing framework such as SPARK. However, the embodiments disclosedherein are not limited to an implementation that uses SPARK. Instead,any open source or proprietary computing framework running on acomputing device that facilitates iterative, interactive, and/orexploratory data analysis coordinated with other computing devices canbe employed to run the modules 218 for the query coordinator 1004 toapply search queries to the distributed data systems.

As a non-limiting example, as part of processing a query, a node 1006can receive instructions from a query coordinator 1004 to perform one ormore tasks. For example, the node 1006 can be instructed to intake datafrom a particular dataset source, parse received data from a datasetsource to identify relevant data in the dataset, collect partial resultsfrom the parsing, join results from multiple datasets, or communicatepartial or completed results to a destination, etc. In some cases, theinstructions to perform a task can come in the form of a DAG. Inresponse, the node 1006 can determine what task it is to perform in theDAG, and execute it.

As part of performing the assigned task, the node 1006 can determine howmany processors 1106 to allocate to the different tasks. In someembodiments the node can determine that all processors 1106 are to beused for a particular task or only a subset of the processors 1106. Incertain embodiments, each processor 1106 of the node 1006 can be used asa partition to intake, process, or collect data according to a task, orto process data of a partition as part of an intake, process, or collecttask. Upon completion of the task, the node 1006 can inform the querycoordinator 1004 that the task has been completed.

When instructed to intake data, the processors 1106 of the node 1006 canbe used to communicate with a dataset source (non-limiting examples:external data sources 1018, indexers 206, common storage, queryacceleration data store 1008, ingested data buffer, etc.). Once the node1006 is in communication with the dataset source it can intake the datafrom the dataset source. As described in greater detail below, in someembodiments, multiple partitions of a node (or different nodes) can beassigned to intake data from a particular source.

When instructed to parse or otherwise process data, the processors 1106of the node 1006 can be used to review the data and identify portions ofthe data that are relevant to the query. For example, if a queryincludes a request for events with certain errors or error types, theprocessors 1106 of the node 1006 can parse the incoming data to identifydifferent events, parse the different events to identify error fields orerror keywords in the events, and determine the error type of the error.In some cases, this processing can be similar to the processingdescribed in greater detail above with reference to the indexers 206processing data to identify relevant results in the data stores 208.

When instructed to collect data, the processors 1106 of the node 1006can be used to receive data from dataset sources or processing nodes.With continued reference to the error example, a collector partition, orprocessor 1106 can collect all of the errors of a certain type from oneor more parsing partitions or processors 1106. For example, if there areseven possible types of errors coming from a particular dataset source,a collector partition could collect all type 1 errors (or events with atype 1 error), while another collector partition could collect all type2 errors (or events with a type 2 error), etc.

When instructed to join results from multiple datasets, the processors1106 of the node 1006 can be used to receive data corresponding to twodifferent datasets and combine or further process them. For example, ifdata is being retrieved from an external data source and a data store208 of the indexers 206, join partitions could be used to compare andcollate data from the different data stores in order to aggregate theresults.

When instructed to communicate results to a particular destination, theprocessors 1106 of the node 1006 can be used to prepare the data forcommunication to the destination and then communicate the data to thedestination. For example, in communicating the data to a particulardestination, the node 1006 can communicate with the particulardestination to ensure the data will be received. Once communication withthe destination has been established, the partition, or processorassociated with the partition, can begin sending the data to thedestination. As described in greater detail below, in some embodiments,data from multiple partitions of a node (or different nodes) can becommunicated to a particular destination. Furthermore, the nodes 1006can be instructed to transform the data so that the destination canproperly understand and store the data. Furthermore, the nodes cancommunicate the data to multiple destinations. For example, one copy ofthe data may be communicated to the query coordinator 1004 and anothercopy can be communicated to the query acceleration data store 1008.

The system 1001 is scalable to accommodate any number of worker nodes1006. As such, the system 1001 can scale to accommodate any number ofdistributed data systems upon which a search query can be applied andthe search results can be returned to the search head and presented in aconcise or comprehensive way for an analyst to obtain insights into biddata that is greater in scope and provides deeper insights compared toexisting systems.

4.1.1. Serialization/Deserialization

In some cases, the serialization/deserialization module 1112 cangenerate and transmit serialized event groups. An event group caninclude the following information: number of events in the group, headerinformation, event information, and changes to the cache or cachedeltas. The serialization/deserialization module 1112 can identify thedifferences between the pieces of information using a type code ortoken. In certain cases, the type code can be in the form of a typebyte. For example, prior to identifying header information, theserialization/deserialization module 1112 can include a header type codeindicating that header information is to follow. Similarly, type codescan be used to identify event data or cache deltas.

The header information can indicate the number and order of fields inthe events, as well as the name of each field. Similarly, the eventinformation for each event can include the number of fields in theevent, as well as the value for that field. The cache deltas canidentify changes to make to the cache relied upon toserialize/deserialize the data.

As part of generating the group and serializing the data, theserialization/deserialization module 1112 can determine the number ofevents to group, determine the order and field names for the fields inthe events of the group, parse the events, determine the number offields for each event, identify and serialize serializable field valuesin the event fields, and identify cache deltas. In some cases, theserialization/deserialization module 1112 performs the various tasks ina single pass of the data, meaning that it performs the identification,parsing, and serializing during a single review of the data. In thismanner, the serialization/deserialization module 1112 can operate onstreaming data and avoid adding delay to theserialization/deserialization process.

In some embodiments, an event group includes an identifier indicatingthe number of events in the group followed by a header type code and anumber of fields indicating the number of fields in the events. For eachfield designated by the header, the event group can include a type codeindicating whether the field name is already stored in cache or a typecode indicating that the field name is included. Depending on the typecode, the event group can include an identifier or the field name. Forexample, if the type code indicates the field name is stored in cache(e.g., a cache code), an identifier can be included to enable areceiving component to lookup the field name using the cache. If thetype code indicates the field name is not stored in cache (e.g., a datacode), the name of the field name can be included.

Similar to the header information, for each event in the event group,the event group can include number of fields in the event. For eachfield of the event, the event group can include a type code indicatingwhether the field name is already stored in cache or a type codeindicating that the field name is included.

As mentioned above, the event group can also include cache deltainformation. The cache delta information can include a cache delta typecode indicating that the cache is to be changed, a number of newentries, and a number of dropped entries. For each new entry the cachedelta information can include the data or string being cached, and anidentifier for the data. For each entry being dropped, the cache deltainformation can include the identifier of the cache entry to be dropped.

As a non-limiting example, consider the following portions of events:

ronnie.sv.splunk.com, access_combined, SALE, World of Cheese, 14.95

ronnie.sv.splunk.com, access_combined, NO SALE, World of Cheese, 16.75

ronnie.sv.splunk.com, access_combined, SALE, World of Cheese

ronnie.sv.splunk.com, access_combined, SALE, Fondue Warrior, 20.95

In serializing the above-referenced events, theserialization/deserialization module 1112 can determine that the fieldnames for the events are source, sourcetype, sale_type, company name,and price and that this information is not in cache. Theserialization/deserialization module 1112 can then generate thefollowing event group:

4 (number of events) Header_Code 5 (number Data_Code “source” of fields)Data_Code “sourcetype” Data_Code “sale_type” Data_Code “company name”Data_Code “price” Cache_Delta_Code 5 (entries “source” x15 to add)“sourcetype” x16 “sale_type” x17 “company name” x18 “price” x19 0(entries to drop) Event_Code 5 (number Data_Code “ronnie.sv.splunk.com”of fields Data_Code “access_combined” in event) Data_Code “SALE”Data_Code “World of Cheese” Data_Code “14.95” Cache_Delta_Code 5 (number“ronnie.sv.splunk.com” x21 of new “access_combined” x22 entries) “SALE”x23 “World of Cheese” x24 “14.95” x25 0 (entries to drop) Event_Code 5(number Cache_Code x21 of fields Cache_Code x22 in event) Data_Code “NOSALE” Cache_Code x24 Data_Code “16.75” Cache_Delta_Code 2 (entries “NOSALE” x26 to add) “16.75” x27 0 (entries to drop) Event_Code 4 (numberCache_Code x21 of fields Cache_Code x22 in event) Cache_Code x23Cache_Code x24 Event_Code 5 (number Cache_Code x21 of fields Cache_Codex22 in event) Cache_Code x23 Data_Code “World of Cheese” Data_Code“20.95” Cache_Delta_Code 2 (number “World of Cheese” of new “20.95”entries) 1 (entry x25 to drop)

By generating the group, the serialization/deserialization module 1112can reduce the amount of data communicated for each group. For example,instead of transmitting the string “ronnie.sv.splunk.com” each time, theserialization/deserialization module 1112 serializes it and thencommunicates the cache ID thereafter.

Entries can be added or dropped using a variety of techniques. In somecases, every new field value is cached. In certain cases, a field valueis cached after it has been identified a threshold number of times.Similarly, an entry can be dropped after a threshold number of events orevent groups have been processed without the particular value beingidentified. As a non-limiting example, the serialization/deserializationmodule 1112 can track X values at a time in a cache C and track up to Yvalues at a time that are not cached and how many time those values havebeen identified in a candidate set D. When a value is received, if it isin the cache C, then the identifier can be returned. If the value is notin the cache C, then it can be added to D. If Y has been reached in D,then the least recently used value can be dropped. If the count of thevalue in D satisfies a threshold T, then it can be moved to the cache Cand receive an identifier. If the size of C is more than X, then theleast recently used value in C can be dropped.

In some embodiments, the cache is built as the data is processed, andchanges are transmitted as they occur. For example, the receiver canstart with an empty cache, and apply each delta as it comes along. Asmentioned above, each delta can have two sections: new entries, anddropped entries. In certain embodiments, the receiver (or deserializer)does not drop cache entries until told to do so, otherwise, it may notbe able interpret identifiers received from the serializer. In suchembodiments, the serializer performs cache maintenance by informing thedeserializer when to drop entries. Upon receipt of such a command, thedeserializer can remove the identified entries.

4.2. Search Process Master

As mentioned above, the search process master 1002 can perform variousfunctions to reduce the workload of the search head 210. For example,the search process master 1002 can parse an incoming query and allocatethe query to a particular query coordinator 1004 for execution or spawnan additional query coordinator 1004 to execute the query. In addition,the search process master 1002 can track and store information regardingthe system 1001, queries, external data stores, etc., to aid the querycoordinator 1004 in processing and executing a particular query. In someembodiments, the search process master 1002.

In some cases, the search process master 1002 can determine whether aquery coordinator 1004 should be spawned based on user information. Forexample, for data protection or isolation, the search process master1002 can spawn query coordinators 1004 for different users. In addition,the search process master 1002 can spawn query coordinators 1004 if itdetermines that a query coordinator 1004 is over utilized.

In some cases, to accomplish these various tasks the search processmaster 1002 can include a workload advisor 1010, workload catalog 1012,node monitor 1014, and dataset compensation module 1016. Althoughillustrated as being a part of the search process master 1002, it willbe understood that any one or any combination of these components can beimplemented separately or included in one or more query coordinators1004. Furthermore, although illustrated as individual components, itwill be understood that any one or any combination of the workloadadvisor 1010, workload catalog 1012, node monitor 1014, and datasetcompensation module 1016 can be implemented by the same machine,processor, or computing device.

As a brief introduction, the workload advisor 1010 can be used toprovide resource allocation recommendations to a query coordinator 1004for processing queries, the workload catalog 1012 can store data relatedto previous queries, the node monitor 1014 can receive information fromthe worker nodes 1006 regarding a current status and/or utilization rateof the nodes 1006, and the dataset compensation module 1016 can be usedby the query coordinator 1004 to enhance interactions with external datasources.

4.2.1 Workload Catalog

The workload catalog 1012 can store relevant information to aid theworkload advisor 1010 in providing a resource allocation recommendationto a query coordinator 1004. As queries are received and processed bythe system 1001, the workload catalog 1012 can store relevantinformation about the queries to improve the workload advisor's 1010ability to recommend the appropriate amount of resources for each query.For example, the system 1001 can track any one or any combination of thefollowing data points about a query: which dataset sources wereaccessed, what was accessed in each dataset source (particular tables,buckets, etc.), the amount of data retrieved from the dataset sources(individually and collectively), the time taken to obtain the data fromthe dataset sources, the number of nodes 1006 used to obtain the datafrom each dataset source, the utilization rate of the nodes 1006 whileobtaining the data from the dataset source, the number oftransformations or phases (processing, collecting, reducing, joining,branching, etc.) performed on the data obtained from the datasetsources, the time to complete each transformation, the number of nodes1006 assigned to each phase, the utilization rate of each node 1006assigned to the particular phase, the processing performed by the querycoordinator 1004 on results (individual or aggregatee), time to store ordeliver results to a particular destination, resources used tostore/deliver results, total time to complete query, time of day ofquery request, etc. Furthermore, the workload catalog can includeidentifying information corresponding to the datasets with which thesystem interacts (e.g., indexers, common storage, ingested data buffer,external data sources, query acceleration data store, etc.). Thisinformation can include, but is not limited to, relationships betweendatasets, size of dataset, rate of growth of dataset, type of data,selectivity of dataset, provider of dataset, indicator for privateinformation (e.g., personal health information, etc.), trustworthinessof a dataset, dataset preferences, etc.

The workload catalog 1012 can collect the data from the variouscomponents of the system 1001, such as the query coordinator 1004,worker nodes 1006, indexers 206, etc. For example, for each taskperformed by each node 1006, the node 1006 can report relevant timingand resource utilization information to the query coordinator 1004 ordirectly to the workload catalog 1012. Similarly, the query coordinator1004 can report relevant timing, usage, and data information for eachphase of a search, each transformation of data, or for a total query.

Using the information collected in the workload catalog 1012, theworkload advisor 1010 can estimate the compute cost to perform aparticular data transformation or query, or to access a particulardataset. Further, the workload advisor can determine the amount ofresources (nodes, memory, processors, partitions, etc.) to recommend fora query in order to provide the results within a particular amount oftime.

4.2.2 Node Monitor

The node monitor 1014 can also store relevant information to aid theworkload advisor 1010 in providing a resource allocation recommendation.For example, the node monitor 1014 can track and store informationregarding any one or any combination of: total number of processors ornodes in the system 1001, number of processors or nodes that are notavailable or not functioning, number of available processors or nodes,utilization rate of the processors or nodes, number of worker nodes,current tasks being completed by the worker nodes 1006 or processors,estimated time to complete a task by the nodes 1006 or processors,amount of available memory, total memory in the system 1001, tasksawaiting execution by the nodes 1006 or processors, etc.

The node monitor 1014 can collect the relevant information bycommunicating with the monitoring module 1110 of each node 1006 of thesystem 1001. As described above, the monitoring modules 1110 of eachnode 1006 can report relevant information about the node state andutilization rate. Using the information from the node monitor 1014, theworkload advisor 1010 can ascertain the general state of any particularprocessor, node, or the system 1001, and determine the number of nodes1006 or processors 1006 available for a particular task or query.

4.2.3 Dataset Compensation

As discussed above, the external data sources 1018 with which the system1001 can interact vary significantly. For example, some external datasource may have processing capabilities that can be used to perform someprocessing on the data that resides there prior to communicating thedata to the nodes 1006. In addition, the external data sources 1018 maysupport parallel reads from multiple partitions. Conversely, otherexternal data sources 1018 may not be able to perform much, if any,processing on the data contained therein and/or may only be able toprovide serial reads from a single partition. Additionally, eachexternal data source 1018 may have particular requirements forinteracting with it, such as a particular API, throttling requirements,etc. Further, the type and amount of data stored in each external datasource 1018 can vary significantly. As such, the system's 1001interaction with the different external data sources 1018 can varysignificantly.

To aid the system 1001 in interacting with the different external datasources 1018, the dataset compensation model 1016 can include relevantinformation related to each external data source 1018 with which thesystem 1001 can interact. For example, the dataset compensation model1016 can include any one or any combination of: the amount of datastored in an external data source 1018, the type of data stored in anexternal data source, query commands supported by an external datasource (e.g., aggregation, filtering ordering), query translator totranslate a query into tasks supported by an external data source, thefile system type and hierarchy of the external data source 1018, numberof partitions supported by an external data source 1018, endpointlocations (e.g., location of processing nodes or processors), throttlingrequirements (e.g., number and rate at which requests can be sent to theexternal data source), etc.

The information about each external data source 1018 can be collected ina variety of ways. In some cases, some of the information about theexternal data source 1018 can be received when a customer sets up theexternal data source 1018 for use with the system 1001. For example, acustomer can indicate the type of external data source 1018 e.g., MySQL,PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra,Mongo DB, cloud storage like Amazon S3 HDFS, etc. Based on thisinformation, the system 1001 can determine certain characteristics aboutthe external data store 1018, such as whether it supports multiplepartitions.

In addition, as discussed herein, different dataset sources havedifferent capabilities. For example, not only can different datasetssources support a different number of partitions, but the datasetsources can support different functions. For example, some datasetsources may be capable of data aggregation, filtering, or ordering,etc., while others may not be. The dataset compensation module 1016 canstore the capabilities of the different dataset sources to aid inproviding a seamless experience to users.

In certain cases, the system 1001 can collect relevant information aboutan external data source by communicating with it. For example, the querycoordinator 1004 or a worker node 1006 can interact with the externaldata source 1018 to determine the number of partitions available foraccessing data. In some cases, the number of available partitions maychange as computing resources on the external data source 1018 becomeavailable or unavailable, etc. In addition, when the system 1001accesses the external data source 1018 as part of a query it can trackrelevant information, such as the tables or amount of data accessed,tasks that the external data source was able to perform, etc. Similarly,the system 1001 can interact with an external data source 1018 toidentify the endpoint that will handle any subqueries and its location.The endpoint and endpoint location may change depending on the subquerythat is to be run on the external data source. Accordingly, in someembodiments, the system 1001 can request endpoint information with eachquery that is to access the particular external data source.

Using the information about the external data sources 1018, a querycoordinator 1004 can determine how to interact with it and how toprocess data obtained from the external data source 1018. For example,if an external data source 1018 supports parallel reads, the querycoordinator 1004 can allocate multiple partitions to read the data fromthe external data source 1018 in parallel. In some embodiments, thequery coordinator 1004 can allocate sufficient partitions or processors1106 to establish a 1:1 relationship with the available partitions atthe external data source 1018. Similarly, if the external data source1018 can perform some processing of the data, the query coordinator 1004can use the information from the dataset compensation module 1016 totranslate the query into commands understood by the external data source1018 and push some processing to the external data source 1018, therebyreducing the amount of system 1001 resources (e.g., nodes 1006) used toprocess the query.

Furthermore, in some cases, using the dataset compensation module 1016,the query coordinator can determine the amount of data in the differentexternal data sources that will be accessed by a particular query. Usingthat information, the query coordinator 1004 can intelligently interactwith the external data sources 1018. For example, if the querycoordinator 1004 determines that data with similar characteristics intwo external data sources are to be accessed and the data from each willeventually be combined, the query coordinator 1004 can first interactwith or query the external data source 1018 that includes less data andthen using information gleaned from that data prepare a more narrowlytailored query for the external data source 1018 with more data.

As a specific example, suppose a user wants to identify the source of aparticular error using information from an HDFS data source and anOracle data source, but does not know what the error is or whatgenerated it. To do so, the user enters a query that includes a requestto identify errors generated within a particular timeframe and stored inan HDFS data source and an Oracle data source and then correlate theerrors based on the error source. Based on the query, the querycoordinator 1004 determines that a union operation is to be performed onthe data from the HDFS data source and the Oracle data source based onthe source of the errors.

Additionally, suppose that the dataset compensation module 1016 hasidentified the HDFS data source as being relatively small and identifiedthe Oracle data source as being significantly larger than the HDFS datasource. Accordingly, based on the information in the datasetcompensation module 1016, the query coordinator 1004 can instruct thenodes 1006 to first intake and process the data from the HDFS datasource. Suppose that by doing so, the nodes 1006 determine that the HDFSdata source only includes fifty types of errors in the specifiedtimeframe from ten sources. Accordingly, using that information, thequery coordinator 1004 can instruct the nodes 1006 to limit the intakeof data from the Oracle data store based on the error type and/or thesource based on the error types and sources identified by firstanalyzing the HDFS data source.

As such, the query coordinator 1004 can reduce the amount of datarequested by the Oracle data store and the amount of processing neededto obtain the relevant result. For example, if the Oracle data storeincluded two hundred error types from one hundred sources, the querycoordinator 1004 avoided having to intake and process the data from allone hundred sources. Instead only the data from sources that matched theten sources from the HDFS data source were requested and processed bythe nodes 1006.

4.3. Query Coordinator

The query coordinator(s) 1004 can act as the primary coordinator orcontroller for queries that are assigned to it by the search head 210 orsearch process master 1002. As such, the query coordinator can process aquery, identify the resources to be used to execute the query, controland monitor the nodes to execute the query, process aggregate results ofthe query, and provide finalized results to the search head 210 orsearch process master 1002 for delivery to a client device 404.

4.3.1. Query Processing

Upon receipt of a query, the query coordinator 1004 can analyze thequery. In some cases analyzing the query can include verifying that thequery is semantically correct or performing other checks on the query todetermine whether it is executable by the system. In addition, the querycoordinator 1004 can analyze the query to identify the dataset sourcesthat are to be accessed and to define an executable search process. Forexample, the query coordinator 1004 can determine whether data from theindexers 206, external data sources 1018, query acceleration data store1008, or other dataset sources (e.g., common storage, ingested databuffers, etc.) are to be accessed to obtain the relevant datasets.

As part of defining the executable search process, the query coordinator1004 can identify the different entities that can perform someprocessing on the datasets. For example, the query coordinator 1004 candetermine what portion(s) of the query can be delegated to the indexers206, nodes 1006, and external data sources 1018, and what portions ofthe query can be executed by the query coordinator 1004, search processmaster 1002, or search head 210. For tasks that can be completed by theindexers 206, the query coordinator 1004 can generate task instructionsfor the indexers 206 to complete, as well as instructions to route allresults from the indexers 206 to the nodes 1006. For tasks that can becompleted by the external data sources 1018, the query coordinator 1004can use the dataset compensation module 1016 to generate taskinstructions for the external data sources 1018 and to determine how toset up the nodes 1006 to receive data from the external data sources1018.

In addition, as part of defining the executable search process, thequery coordinator 1004 can generate a logical directed acyclic graph(DAG) based on the query. FIG. 12 is a diagram illustrating anembodiment of a DAG 2000 generated as part of a search process. In theillustrated embodiment, the DAG 2000 includes seven vertices and sixedges, with each edge directed from one vertex to another, such that bystarting at any particular vertex and following a consistently-directedsequence of edges the DAG 2000 will not return to the same vertex.

Here, the DAG 2000 can correspond to a topological ordering of searchphases, or layers, performed by the nodes 1006. As such, a sequence ofthe vertices can represent a sequence of search phases such that eachedge is directed from earlier to later in the sequence of search phases.For example, the DAG 2000 may be defined based on a search string foreach phase or metadata associated with a search string. The metadata maybe indicative of an ordering of the search phases such as, for example,whether results of any search string depend on results of another searchstring such that the later search string must follow the former searchstring sequentially in the DAG 2000.

In the illustrated embodiment of FIG. 12, the DAG 2000 can correspond toa query that identifies data from two dataset sources that are to becombined and then communicated to different locations. Accordingly, theDAG 2000 includes intake vertices 1202, 1208, a process vertex 1204,collect vertices 1206, 1210, a join vertex 1212, and a branch vertex1214.

Each vertex 1202, 1204, 1206, 1208, 1210, 1212, 1214 can correspond to asearch phase performed using one or more partitions or processors 1106of one or more nodes 1006 on a particular set of data. For example, theintake, process, and collect vertices 1202, 1204, 1206 can correspond todata search phases, or transformations, on data received from a firstdataset source. More specifically, the intake phase or vertex 1202 cancorrespond to one or more partitions that receive data from the firstdataset source, the process phase 1204 can correspond to one or morepartitions used to process the data received by the partitions at theintake phase 1202, and the collect phase 1206 can correspond to one ormore partitions that collect the results of the processing by thepartitions in the process phase 1204.

Similarly, the intake and collect vertices 1208, 1210 can correspond todata search phases performed using one or more partitions or processors1106 on data received from a second dataset source. For example, theintake phase 1208 can correspond to one or more partitions that receivedata from the second dataset source and the collect phase 1210 cancorrespond to one or more partitions that collect the results from thepartitions in the intake phase 1208.

The join and branch phases 1212, 1214 can correspond to data searchphases performed using one or more partitions or processors 1106 on datareceived from the different branches of the DAG 2000. For example, thejoin phase 1212 can correspond to one or more partitions used to combinethe data received from the partitions in the collect phases 1206, 1210.The branch phase 1214 can correspond to one or more partitions used tocommunicate results of the join phase 1212 to one or more destinations.For example, the partitions in the branch phase 1214 can communicateresults of the query to the query coordinator 1004, an external datasource 1018, accelerated data source 1008, ingested data buffer, etc.

It will be understood that the number, order, and types of search phasesin the DAG 2000 can be determined based on the query. As a non-limitingexample, consider a query that indicates data is to be obtained fromcommon storage and an Oracle database, collated, and the results sent tothe query coordinator 1004 and an HDFS data store. In this example, inresponse to determining that the common storage do not provideprocessing capabilities, the query coordinator 1004 can generatevertices 1202, 1204, 1206 indicating that an intake phase 1202, processphase 1204, and collect phase 1206 will be used to process the data fromthe common storage sufficiently to be combined with data from the Oracledatabase. Similarly, based on a determination that the Oracle databasecan perform some processing capabilities, the query coordinator cangenerate vertices 1208, 1210 indicating that an intake phase 1208 andcollect phase 1210 will be used to sufficiently process the data fromthe Oracle database for combination with the data from the commonstorage.

The query coordinator 1004 can further generate the join phase 1212based on the query indicating that the data from the Oracle database andcommon storage is to be collated or otherwise combined (e.g., joined,unioned, etc.). In addition, based on the query indicating that theresults of the combination are to be communicated to the querycoordinator 1004 and the HDFS data store, the query coordinator 1004 cangenerate the branch phase 1214. As mentioned above, in each phase, thequery coordinator 1004 can allocate one or more partitions to performthe particular search phase.

It will be understood that the DAG 2000 is a non-limiting example of thesearch phases that can be included as part of a search process. In somecases, depending on the query, the DAG 2000 can include fewer or morephases of any type. For example, the DAG 2000 can include fewer or moreintake phases depending on the number of dataset sources. Additionally,depending on the particular processing requirements of a query, the DAG2000 can include multiple processing, collect, join, union, stats, orbranch phases, in any order.

In addition to determining the number and types of search phases for asearch process, the query coordinator 1004 can calculate the relativecost of each phase of the search process, determine the amount ofresources to allocate for each phase of the search process, generatetasks and instructions for particular nodes to be assigned to aparticular search process, generate instructions for dataset sources,generate tasks for itself and/or the search head 210, etc.

To calculate the relative cost of each phase of the search process anddetermine the amount of resources to allocate for each phase of thesearch process, the query coordinator 1004 can communicate with theworkload advisor 1010, workload catalog 1012, and/or the node monitor1014. As described previously, the workload advisor 1010 can use thedata collected in the workload catalog 1012 to determine the cost of aquery or an individual transformation or search phase of a searchprocess and to provide a resource allocation recommendation.Furthermore, the workload advisor 1010 can use the data from the nodemonitor module 1014 to determine the available resources in the system1001. Using this information, the query coordinator 1004 can determinethe cost for each search phase, the amount of resources available forallocation, and the amount of resources to allocate for each searchphase.

In determining the amount of resources to allocate for each searchphase, the query coordinator 1004 can also generate the tasks andinstructions for each node 1006. The instructions can include computerexecutable instructions that when executed by the node 1006 cause thenode 1006 to perform the task assigned to it by the query coordinator1004. For example, for nodes 1006 that are to be assigned to an intakephase 1202, 1208, the query coordinator 1004 can generate instructionson how to access a particular dataset source, what instructions are tobe sent to the dataset source, what to do with the data received fromthe dataset source, where do send the received data, how to perform anyload balancing or other tasks assigned to it, etc. For nodes 1006 thatare to process data in the process phase 1204, the query coordinator1004 can generate instructions indicating how to parse the receiveddata, relevant fields or keywords that are to be identified in the data,what to do with the identified field and keywords, where to send theresults of the processing, etc. Similarly, for nodes 1006 in the collectphases 1206, 1210, join phase 1212, or branch phase 1214, the querycoordinator 1004 can generate task instructions so that the nodes 1006are able to perform the task assigned to that particular phase. The taskinstructions can tell the nodes 1006 what data they are to process, howthey are to process the data, where they are to route the results of theprocessing, either between each other or to another destination. In somecases, the query coordinator 1004 can generate the tasks andinstructions for all nodes 1006 or processors 1106 and send theinstructions to all of the allocated nodes 1006 or processors 1106.Between them, the nodes 1006 or processors 1106 can determine or assignpartitions to be used to help execute the different instructions andtasks. The instructions sent to the nodes 1006 or processors 1106 caninclude additional parameters, such as a preference to use processors1106 partitions on the same machine for subsequent tasks. Suchinstructions can help reduce the amount of data communicated over thenetwork, etc.

In some embodiments, to generate instructions for the dataset sources,the query coordinator 1004 can use the dataset compensation module 1016.As described previously, the dataset compensation module 1016 caninclude relevant data about external data sources including, inter alia,processing abilities of the external dataset sources, number ofpartitions of the external dataset sources, instruction translators,etc. Using this information, the query coordinator 1004 can determinewhat processing to assign to the external data sources, and generateinstructions that will be understood by the external data sources. Inaddition, the query coordinator 1004 can have access to similarinformation about other dataset sources and/or communicate with thedataset sources to determine their processing capabilities and how tointeract with them (non-limiting examples: number of partitions to use,processing that can be pushed to the dataset source, etc.). Similarly,the query coordinator 1004 can determine how to interact with thedataset destinations so that the datasets can be properly sent to thecorrect location in a manner that the destination can store themcorrectly.

In some cases, the query coordinator 1004 can interact with onepartition of the external dataset source using multiple partitions. Forexample, the query coordinator 1004 can allocate multiple partitions tointeract with a single partition of the external dataset source. Thequery coordinator 1004 can break up a query or a subquery into multipleparts. Each part can be assigned to a different partition, which cancommunicate the subqueries to the partition of the external datasetsource. Thus, unbeknownst to the external dataset source, it canconcurrently process data from a single query.

Furthermore, the query coordinator 1004 can determine the order forconducting the search process. As mentioned above, in some embodiments,the query coordinator 1004 can determine that processing data from onedataset source could speed up the search process as a whole(non-limiting example: using data from one dataset source to generate amore targeted search of another dataset source). Accordingly, the querycoordinator 1004 can determine that one or more search phases are to becompleted first and then based on information obtained from the searchphase, additional search phases are to be initiated. Similarly, otheroptimizations can be determined by the query coordinator 1004. Suchoptimizations can include, but are not limited to, pushing processing tothe edges (e.g., to external data sources, etc.), identifying fields ina query that are key to the query and reducing processing based on theidentified field (e.g., if a relevant field is identified in a finalprocessing step, use the field to narrow the set of data that issearched for earlier in the search process), allocating the query tonodes that are physically close to each other or on the same machine,etc.

4.3.2. Query Execution and Node Control

Once the query is processed and the search scheme determined, the querycoordinator 1004 can initiate the query execution. In some cases, ininitiating the query, the query coordinator 1004 can communicate thegenerated task instructions to the various locations that will processthe data. For example, the query coordinator 1004 can communicate taskinstructions to the indexers 206, based on a determination that theindexers 206 are to perform some amount of processing on the dataset.Similarly, the query coordinator 1004 can communicate task instructionsto the nodes 1006, external data sources 1018, query acceleration datastore 1008, common storage, and/or ingested data buffer, etc.

In some embodiments, rather than communicating with the various datasetsources, the query coordinator 1004 can generate task instructions forthe nodes 1006 to interact with the dataset sources such that thedataset sources receive any task instructions from the nodes 1006 asopposed to the query coordinator 1004. For example, rather thancommunicating the task instructions directly to a dataset source, thequery coordinator 1004 can assign one or more nodes 1006 to communicatetask instructions to the external data sources 1018, indexers 206, orquery acceleration data store 1008. In certain embodiments, the querycoordinator 1004 can communicate the same search scheme or taskinstructions to the nodes 1006 or partitions of the nodes 1006 that havebeen allocated for the query. The allocated nodes 1006 or partitions ofthe nodes 1006 can then assign different groups to perform differentportions of the search scheme.

Upon receipt of the task instructions, the dataset sources and nodes1006 can begin operating in parallel. For example, if task instructionsare sent to the indexers 206 and to the nodes 1006, both can beginexecuting the instructions in parallel. In executing the taskinstructions, the nodes 1006 can organize their processors 1106 orpartitions according to task instructions. For example, some of thenodes 1006 can allocate one or more partitions or processors 1106 aspart of an intake phase, another partition as part of a processingphase, etc. In some cases, all partitions or processors 1106 of a node1006 can be allocated to the same task or to different tasks. In certainembodiments, it can be beneficial to allocate partitions from the samenode 1006 to different tasks to reduce network traffic between nodes1006 or machines 1102.

FIG. 13 is a block diagram illustrating an embodiment of layers ofpartitions implementing various search phases of a query. In some cases,the layers can correspond to search phases in a DAG, such as the DAG2000 described in greater detail above. In the illustrated embodiment ofFIG. 13, based on task instructions received from the query coordinator1004, the nodes 1006 have arranged various partitions to performdifferent search phases on data coming from a dataset source 1302. Asdescribed previously, the dataset source 1302 can correspond to indexers206, external data sources 1018, the query acceleration data store 1008,common storage, an ingested data buffer, or other source of data fromwhich the nodes 1006 can receive data.

As referenced in FIG. 12, the partitions in each layer can interact withthe data based on task instructions received by the query coordinator1004. In the illustrated embodiment of FIG. 13, the partitions in theintake layer 1304 can receive the data from the dataset source 1302,which can be communicated to the partitions in the processing layer 1306in a load-balanced fashion. The partitions in the processing layer 1306can be used to process the data based on the task instructions, whichwere generated based on the query, and the results provided to thepartitions in the collector layer 1308. Similarly, upon completing theirassigned task, the processors associated with the partitions in thecollector layer 1308 can communicate the results of their processing tothe branch layer 1310. In the illustrated embodiment of FIG. 13, thebranch layer 1310 communicates the results received from the partitionsin the collector layer 1308 to a first dataset destination 1314 and topartitions in a storage layer 1312 for storage in a second datasetdestination 1316. It will be understood that fewer or more layers can beincluded as desired, and can be based on the content of the particularquery being executed. Furthermore, it will be understood that the layerscan correspond to different map-reduce procedures or commands. Forexample, as described herein, in the illustrated embodiments, theprocessing layer 1306 can correspond to a map procedure and thecollector layer 1308 can correspond to a reduce procedure. However, asdescribed herein, it will be understand that various layers cancorrespond to map or reduce procedures.

In the illustrated embodiment, four partitions have been allocated tothe intake layer 1304, eight partitions have been allocated to theprocessing layer 1306, five partitions have been allocated to thecollector layer 1308, one partition has been allocated to the branchlayer 1310, and three partitions have been allocated to the storagelayer 1312. However, it will be understood that fewer or more partitionscan be assigned to any layer as desired and fewer or additional layerscan be included. For example, based on a query that indicates multipledataset sources are to be accessed, the query coordinator 1004 canallocate separate intake, processing, and collector layers 1304, 1306,1308 for each dataset source 1302. Furthermore, based on the querycommands, the query coordinator can allocate additional layers, such asa join layer to combine data received from multiple dataset sources,etc.

In determining the number of partitions and/or processors 1106 for eachsearch phase or layer, the query coordinator 1004 can use the workloadadvisor 1010 and/or dataset compensation module 1016. For example, theworkload advisor 1010 can use historical data about executing individualsearch phases in queries to recommend an allocation scheme that providessufficient resources to process the query in a reasonable amount oftime.

In some cases, the query coordinator 1004 can allocate partitions forthe intake layer 1304 and storage layer 1312 based on information aboutthe number of partitions available for reading from the dataset source1302 and writing data to the dataset destination 1316, respectively. Thequery coordinator 1004 can obtain the information about the datasetsource 1302 or dataset destination 1316 from a number of locations,including, but not limited to, the workload catalog 1012, the datasetcompensation module 1016, or from the dataset source 1302 or datasetdestination 1316 itself. The information can inform the querycoordinator 1004 as to the number of partitions available for readingfrom the dataset source 1302 and writing to the dataset destination1316.

In some cases, the query coordinator 1004 can allocate partitions in theintake layer 1304 or the storage layer 1312 to have a one-to-one,one-to-many, or many-to-one correspondence with partitions in thedataset source 1302 or dataset destination 1316, respectively. Thecorrespondence between the partitions in the intake or storage layer1304, 1312 and the partitions in the dataset source or destination 1302,1316, respectively, can be based on a threshold number of partitions,the type of the dataset source/destination, etc.

In certain embodiments, if the query coordinator 1004 determines thatthe dataset source 1302 (or dataset destination 1316) has a number ofpartitions that satisfies a threshold number of partitions or determinesthat the number of partitions of the dataset source 1302 (or datasetdestination 1316) can be matched without overextending the nodes 1006,the query coordinator 1004 can allocate partitions in the intake layer1304 (or storage layer 1312) to have a one-to-one correspondence topartitions in the dataset source 1302 (or dataset destination 1316). Thenumber of partitions that satisfy the threshold number of partitions canbe determined based on the number of nodes 1006 or processors 1106 inthe system 1001, the number of available nodes 1006 in the system 1001,scheduled usage of nodes 1006, etc. Accordingly, the threshold number ofpartitions can be dynamic depending on the status of the processors1106, nodes 1006, or the system 1001. For example, if a large number ofnodes 1006 are available, the threshold number of nodes can be larger,whereas, if only a relatively small number of nodes 1006 are available,the threshold number can be smaller. Similarly, if the workload advisor10010 expects a large number of queries in the near term it can allocatefewer partitions to an individual query. Alternatively, if the workloadadvisor 10010 does not expect many queries in the near term it canallocate a greater number of partitions to an individual query.

In some cases, the query coordinator 1004 can determine whether to matchthe number of partitions in the dataset source 1302 or datasetdestination 1316 with corresponding partitions in the intake layer 1304or storage layer 1312, respectively, based on the type of the datasetsource 1302 or dataset destination 1316. For example, the querycoordinator 1004 can determine there should be a one-to-onecorrespondence of intake layer 1304 partitions to dataset source 1302partitions (or storage layer 1312 partitions to dataset destination 1316partitions) when the dataset source 1302 (or dataset destination 1316)is an external data source or ingested data buffer and that there shouldbe a one-to-multiple correspondence when the dataset source 1302 (ordataset destination 1316) is indexers 206, common storage, queryacceleration data store 1008, etc.

As a non-limiting example, if the dataset source 1302 is an externaldata source or ingested data buffer with four partitions and the querycoordinator 1004 determines that it can support a one-to-onecorrespondence, the query coordinator 1004 can allocate four partitionsto the intake layer 1304, as illustrated in FIG. 13. Similarly, if thedataset destination 1316 is an external data source or ingested databuffer with three partitions and the query coordinator 1004 determinesthat it can support a one-to-one correspondence, the query coordinator1004 can allocate three partitions to the storage layer 1312, asillustrated in FIG. 13. As another non-limiting example, if the datasetsource 1302 (or dataset destination 1316) is indexers 206, commonstorage, or query acceleration data stores 1008 with hundreds ofpotential partitions, and/or the query coordinator 1004 determines thatit cannot support a one-to-one correspondence, it can allocate the fourpartitions to the intake layer 1304 (or the three partitions to thestorage layer 1312), as illustrated in FIG. 13.

In addition, during intake of the data from the dataset source 1302, thequery coordinator 1004 can dynamically adjust the number of partitionsin the intake layer 1304. For example, if an additional partition of thedataset source 1302 becomes available or one of the partitions becomesunavailable, the query coordinator 1004 can dynamically increase ordecrease the number of partitions in the intake layer 1304. Similarly,if the query coordinator 1004 determines that the intake layer 1304 istaking too much time and additional resources are available, it candynamically increase the number of partitions in the intake layer 1304.In addition, if the query coordinator 1004 determines that additionalresources are available or become unavailable, it can dynamicallyincrease or decrease the number of partitions in the intake layer 1304.Similarly, the query coordinator can dynamically adjust the number ofpartitions in the storage layer 1312.

Similar to the intake layer 1304 and storage layer 1312, the querycoordinator 1004 can allocate partitions to the different search layers1306, 1308, 1310 based on information about the query and information inthe workload catalog 1012. For example, the query may include requeststo process the data in a way that is resource intensive. As such, thequery coordinator 1004 can allocate a larger number of partitions and/orprocessors 1106 to the processing layer 1306 or use multiple processinglayers 1306 to process the data. In some cases, more partitions can beallocated to the search layers for queries of larger datasets.

In addition, during execution of the query, the query coordinator 1004can monitor the partitions in the search layers 1306, 1308, 1310 anddynamically adjust the number of partitions in each depending on thestatus of the individual partitions, the status of the nodes 1006, thestatus of the query, etc. In some cases, the query coordinator 1004 candetermine that a significant number of results are being sent to aparticular partition in the collector layer 1308. As such, the querycoordinator 1004 can allocate an additional partition to the collectorlayer and/or instruct that the results from the partitions in theprocessing layer 1306 be distributed in a different manner to reduce theload on the particular partition in the collector layer. In certaincases, the query coordinator 1004 can determine that a partition in theprocessing layer 1306 is not functioning or that there is significantlymore data coming from the dataset source 1302 than was anticipated.Accordingly, the query coordinator 1004 can allocate an additionalpartition 1306 to the processing layer. Conversely, if the querycoordinator 1004 determines that some of the partitions or processors1106 are underutilized, then it can deallocate it from a particularlayer and make it available for other queries, or assign it to adifferent layer, etc. Accordingly, the query coordinator 1004 candynamically allocate and deallocate resources to intake and process thedata from the dataset source 1302 in a time-efficient and performantmanner.

As a non-limiting example, consider a query that includes a request tocount the number of different types of errors in data stored in anexternal data source within a timeframe and to return the results to theuser and store the results in the query acceleration data store 1008.Based on the query, the query coordinator 1004 can generate a DAG thatincludes the intake layer 1304, processing layer 1306, collector layer1308, branch layer 1310, and storage layer 1312. Additionally, based ona determination that the external data source supports four partitions,the query coordinator 1004 allocates four partitions to the intake layer1304. In addition, based on the expected amount of data to be processed,the query coordinator 1004 allocates eight partitions to the processinglayer 1306, and five partitions to the collector layer 1308. Further,based on resource availability and the determination that the datasetdestination is the query acceleration data store 1008, which can supportmore than a threshold number of partitions, the query coordinator 1004allocates three partitions to the storage layer 1312. The taskinstructions for each partition of each search layer can be sent to thenodes 1006, which assign processors 1106 to the various tasks andpartitions. In some cases, the processors 1106 and partitions can have a1:1 correspondence, such that each partition corresponds to oneprocessor. In certain embodiments, multiple partitions can be assignedto a processor 1106 or vice versa. As such, when referred to herein as apartition performing an action, it will be understood that the actioncan be performed by the processor 1106 assigned to that partition.

During execution, the partitions in the intake layer 1304 (or processorsassigned to the partition) communicate with the dataset source 1302 toreceive the relevant data from the partitions of the dataset source1302. The data is then communicated to the partitions in the processinglayer 1306. In the illustrated embodiment, each partition of the intakelayer 1304 communicates data in a load-balanced fashion to twopartitions in the processing layer 1306. The partitions in theprocessing layer 1306 can parse the incoming data to identify eventsthat include an error and identify the type of error.

The partitions in the processing layer 1306 can determine the results tothe partitions in the collector layer 1308. For example, each partitionin the processing layer 1306 can apply a modulo five to the error typein order to attempt to equally separate the results between the fivepartitions in the collector layer 1308. As such, for each error type, apartition in the collector layer 1308 can include the total count oferrors for that type. Depending on the query, in some cases, thepartitions in the collector layer 1308 can also include the event thatincluded the particular error type.

The partitions in the collector layer 1308 can send the results to thepartition in the branch layer 1310. The partition in the branch layer1310 can communicate the results to the query coordinator 1004, whichcan communicate the results to the search head or client device. Inaddition, the branch layer 1310 can communicate the results to thepartitions in the storage layer 1312, which communicate the results inparallel to the query acceleration data store 1008.

Throughout the execution of the query, the query coordinator 1004 canmonitor the partitions in the intake layer 1304, processing layer 1306,collector layer 1308, branch layer 1310, and storage layer 1312. If onepartition becomes unavailable or becomes overloaded, the querycoordinator 1004 can allocate additional resources. Similarly, if apartitions is not being utilized, the query coordinator 1004 candeallocated it from a layer. For example, if a partition on the externaldata source becomes unavailable, a corresponding partition in the intakelayer 1304 may no longer receive any data. As such, the querycoordinator 1004 can deallocate that partition from the intake layer1304. In some embodiments, any change in state of a partition can bereported to the node monitor module 1014, which can be used by the querycoordinator to allocate resources.

4.3.3. Result Processing

Once the nodes 1006 have completed processing the query or particularresults of the query, they can communicate the results to the querycoordinator 1004. The query coordinator 1004 can perform any finalprocessing. For example, in some cases, the query coordinator 1004 cancollate the data from the nodes 1006. The query coordinator 1004 canalso send the results to the search head 210 or to a datasetdestination. For example, based on a command (non-limiting example“into”), the query coordinator 210 can store results in the queryacceleration data store 1008, an external data source 1018, an ingesteddata buffer, etc. In addition, the query coordinator 1004 cancommunicate to the search process master 1002 that the query has beencompleted. In the event all queries assigned to the query coordinator1004 have been completed, the query coordinator can shut down or enter ahibernation state and await additional queries assigned to it by thesearch process master 1002.

4.4. Query Acceleration Data Store

As described herein, a query can indicate that information is to bestored (e.g., stored in non-volatile or volatile memory) in the queryacceleration data store 1008.

As described above, the query acceleration data store 1008 can storeinformation (e.g., datasets) sourced from other dataset sources, suchas, external data sources 1018, indexers 206, ingested data buffers,indexers, and so on. For example, when providing a query, a user canindicate that particular information is to be stored in the queryacceleration data source 1008 (e.g., cached). The information caninclude the results of the query, partial results of the query, data(processed or unprocessed) received from another dataset source via thenodes 1006, etc. Subsequently, the data intake and query system 1001 cancause queries directed to the particular information to utilize thequery acceleration data store 1008. In this way, the stored informationcan be rapidly accessed and utilized.

As an example, the query can indicate that information is to be obtainedfrom the external data sources 1018. Since the external data sources1018 may have potentially high latency, response times to particularqueries, the query can be constrained according to characteristics ofthe external data sources 1018. For example, particular external datasources 1018 may be limited in their processing speed, networkbandwidth, and so on, such that the worker nodes 1006 are required towait longer for information. As described herein, the query cantherefore specify that particular information from the external datasources 1018 (or other dataset sources) be stored in the queryacceleration data store 1008. Subsequent queries that utilize thisparticular information can then be executed more quickly. For example,in subsequent queries the worker nodes 1006 can obtain the particularinformation from the query acceleration data store 1008 rather than fromthe external data source 1018.

An example query can be of a particular form, such as:

-   -   Query=<from [dataset source]>|<[logic]>|[accelerated directive]

In the above example, the query indicates that information is to beobtained from a dataset source, such as an external data source 1018.Optionally, the query can indicate particular tables, documents,records, structured or unstructured information, and so on. As describedabove, the data intake and query system 1001 can process the query anddetermine that the external data source is being referenced. The nextelement of the query (e.g., a request parameter) includes logic to beapplied to the data from the external data source, for example the logiccan be implemented as structured query language (SQL), search processinglanguage (SPL), and so on. As described above, the worker nodes 1006 canobtain the requested data, and apply the logic to obtain information tobe provided in response to the query.

In the above example query, an accelerated directive is included. Forexample, the accelerated directive can be a particular term (e.g., “intoquery acceleration data store”), symbol, and so on, included in thequery. The accelerated directive can optionally be manually included inthe query (e.g., a user can type the directive), or automatically. As anexample of automatically including the directive, a user can indicate ina user interface associated with entering queries that information is tobe stored in the query acceleration data store 1008. As another example,the user's client device or query coordinator 1004 can determine thatinformation is to be stored in the data store 1008. For example, thequery can be analyzed by the client device or query coordinator 1004,and based on a quantity of information being requested, the clientdevice or query coordinator 1004 can automatically include theaccelerated directive (e.g., if greater than a threshold quantity isbeing requested, the directive can be included). Optionally, the dataintake and query system 1001 can automatically store the requestedinformation in the query acceleration data store 1008 without anaccelerated directive in a received query. For example, the query system1001 can automatically store data in the query acceleration data store1008 based on a user ID (e.g., always store results for a particularuser or based on recent use by the user), time of day (e.g., storeresults for queries made at the beginning or end of a work day, etc.),dataset source identity (e.g., store data from dataset source identifiedhas having a slower response time, etc.), network topology (e.g., storedata from sources on a particular network given the network bandwidth,etc.) etc. Although the above example shows the accelerated directive atthe end of the query, it will be understood that it can be placed at anypart of it. In some cases, the result of the command preceding theaccelerated directive corresponds to the data stored in the queryacceleration data store 1008.

Upon receipt of the query, the data intake and query system 1001 (e.g.,the query coordinator 1004) can cause the requested information from thedataset source to be stored in the query acceleration data store 1008.Optionally, the query acceleration data store 1008 can receive theprocessed result associated with the query (e.g., from the worker nodes1006). The query acceleration data store 1008 can then provide theprocessed result to the query coordinator 1004 to be relayed to therequesting client. However, to increase response times, the worker nodes1006 can provide processed information to the query acceleration datastore 1008, and also to the query coordinator 1004. In this way, thequery acceleration data store 1008 can store (e.g., in low latencymemory, or longer latency memory such as solid state storage or diskstorage) the received processed information, while the query coordinator1004 can relay the received processed information to the requestingclient.

The processed result may be stored by the query acceleration data store1008 in association with an identifier, such that the information can beeasily referenced. For example, the query acceleration data store 1008can generate a unique identifier upon receipt of information for storageby the worker nodes 1006. For subsequent queries, the query coordinator1004 can receive the identifier, such that the query coordinator 1004can replace the initial portion with the unique identifier.

In some embodiments, the query coordinator 1004 can generate the uniqueidentifier. For example, the query coordinator can receive informationfrom the query acceleration data store 1008 indicating that it storedinformation. The query coordinator 1004 can maintain a mapping betweengenerated unique identifiers and datasets, partitions, and so on, thatare associated with information stored by the query acceleration datastore 1008. The query coordinator 1004 may optionally provide a uniqueidentifier to the requesting client, such that a user of the requestingclient can re-use the unique identifier. For example, the user's clientcan present a list of all such identifiers along with respective queriesthat are associated with the identifier. The user can select anidentifier, and generate a new query that is based on an associatedquery.

In addition to storing the data or the results or partial results of thequery, the query acceleration data store can store additionalinformation regarding the results. For example, the query accelerationdata store can store information about the size of the dataset, thequery that resulted in the dataset, the dataset source of the dataset,the time of the query that resulted in the dataset, the time range ofdata that was processed to produce the dataset, etc. This informationcan be used by the system 1001 to prompt a user as to what data isstored and can be used in the query acceleration data store, determinewhether portions of an incoming query correspond to datasets in theaccelerate data store, etc. This information can also be stored in theworkload catalog 1012, or otherwise made available to the querycoordinator 1004.

Subsequently, for received queries that reference the processedinformation, the query coordinator 1004 can cause the worker nodes 1006to obtain the information from the query acceleration data store 1008.

For example, a subsequent query can be

-   -   Query=<from [dataset source]>|<[logic]>|<[subsequent_logic]>

In the above query, the query coordinator 1004 can determine that someportion of the data referenced in the query corresponds to data that isstored in the query acceleration data store 1008 (previously storeddata) or was previously processed according to a prior query (e.g., thequery represented above) and the results of the processing stored in thequery acceleration data store 1008. For example, the query coordinator1004 can compare the query to prior queries, and any portion of datathat was referenced in a prior query. The query coordinator 1004 canthen instruct the worker nodes 1006 to obtain the previously stored dataor the results of processing the data from the query acceleration datastore 1008. In some cases, the subsequent query can include an explicitcommand to obtain the data or results from the query acceleration datastore 1008.

Obtaining the previously stored data or results of processing the dataprovides multiple technical advantages. For example, the worker nodes1006 can avoid having to reprocess the data, and instead can utilize theprior processed result. Additionally, the worker nodes 1006 can morerapidly obtain information from the query acceleration data store 1008than, for example, the external data sources 1018. As an example, theworker nodes 1006 may be in communication with the query accelerationdata store 1008 via a direct connection (e.g., virtual networks, localarea networks, wide area networks). In contrast, the worker nodes 1006may be in communication with the external data sources 1018 via a globalnetwork (e.g., the internet).

As a non-limiting example, in some cases, a first query can indicatethat data from a dataset source is to be stored in the queryacceleration data store 1008 with minimal processing by the nodes 1006or without transforming the data from the dataset source. A subsequentquery can indicate that the data stored in the query acceleration datastore 1008 is to be processed or transformed, or combined with otherdata or results to obtain a result. In certain cases, the first querycan indicate that data from the dataset source is to be transformed andthe results stored in the query acceleration data store 1008. Thesubsequent query can indicate that the results stored in the queryacceleration data store 1008 are to be further processed, combined withdata or results from another dataset source, or provided to a clientdevice.

Furthermore, in certain embodiments, the worker nodes 1006 can performany additional processing on the results obtained from the queryacceleration data store 1008, while concurrently obtaining data fromanother dataset source and processing it to obtain additional results.In some cases, the results stored in the query acceleration data store1008 can be communicated to a client device while the nodes concurrentlyobtain data from another dataset source and process it to obtainadditional results. By obtaining, processing, and displaying the resultsof the previously processed data while concurrently obtaining additionaldata to be processed, processing the additional data, and communicatingthe results of processing the additional data, the system 1001 canprovide a more effective responsiveness to a user and decrease theresponse time of a query.

For the subsequent query identified above, the ‘subsequent_logic’ can beapplied by the worker nodes 1006 based on the processed result stored bythe query acceleration data store 1008. The result of the subsequentquery can then be provided to the query coordinator 1004 to be relayedto the requesting client.

The query acceleration data store 1008, as described herein, canmaintain information in low-latency memory (e.g., random access memory)or longer-latency memory. That is, the query acceleration data store1008 can cause particular information to spill to disk when needed,ensuring that the data store 1008 can service large amounts of queries.Since, in some implementations, the low-latency memory can be less thanthe longer-latency memory, the query acceleration data store 1008 candetermine which datasets are to be stored in the low-latency memory. Insome embodiments, to provide this functionality, the query accelerationdata store 1008 can be implemented as a distributed in-memory data storewith spillover to disk capabilities. For example, the data in the queryacceleration data store 1008 can be stored in low-latency volatilememory, and in the event, the capacity of the low-latency volatilememory is reached, the data can be stored to disk.

In some embodiments, the query acceleration data store 1008 can utilizeone or more storage policies to swap datasets between low-latency memoryand longer-latency memory. Additionally, the query acceleration datastore 1008 can flush particular datasets after determining that thedatasets are no longer needed (e.g., the user can indicate that thedatasets can be flushed, or a threshold amount of time can pass).

As an example of a storage policy, the query acceleration data store1008 can store a portion of a dataset in low-latency memory whilestoring a remaining portion in longer-latency memory. In this way, thequery acceleration data store 1008 can have faster access to at least aportion each user's dataset. If a subsequent query is received by thedata intake and query system 1001 that references a stored dataset, thequery acceleration data store 1008 can access the portion of the storeddataset that is in low-latency memory. Since this access is, in general,with low-latency, the query acceleration data store 1008 can quicklyprovide this information to the worker nodes 1006 for processing. At asame, or similar, time, the query acceleration data store 1008 canaccess the longer-latency memory and obtain a remaining portion of thestored dataset. The worker nodes 1006 can then receive this remainingportion for processing. Therefore, the worker nodes 1006 can quicklyrespond to a request, based on the initially received portion from thelow-latency memory. In this way, the user can receive search results ina manner that appears to be in ‘real-time’, that is, the search resultscan be provided in a less than a threshold amount of time (e.g., 1second, 5 seconds, 10 seconds). Subsequent search results can then beprovided upon the worker nodes 1006 processing the portion from thelonger-latency memory.

The above-described storage policy may be based on a size of thedataset(s). For example, an example dataset may be less than athreshold, and the query acceleration data store 1008 may store theentirety of the dataset in low-latency memory. For an example datasetgreater than the threshold, the data store 1008 may store a portion inlow-latency memory. As the size of the dataset increases, the queryacceleration data store 1008 can store an increasingly lesser sizedportion in low-latency memory. In this way, the data store 1008 canensure that large data sets do not consume the low-latency memory.

While the queries described above indicate, a first query that includesan accelerated directive, and a second query that includes the firstquery (e.g., as an initial portion), optionally the data intake andquery system 1001 can receive a first query that is a combination of thefirst query and second query described above. For example, an exampleinitial query can be

-   -   Query=<from [dataset source]>|<[logic]>|[accelerated directive]        |<[subsequent_logic]>

The above example query indicates that the data intake and query system1001 is to obtain information from an example dataset source (e.g.,external data source 1018), process the information, and cause the queryacceleration data store 1008 to store the processed information. Inaddition, subsequent logic is to be applied to the processedinformation, and the result provided to the requesting client 404 a-404n.

FIG. 13 illustrates a branch layer 1310, which for the example querydescribed above, can be utilized to provide information both to thequery acceleration data store 1008 and the data destination 1314 (e.g.,the requesting client). For example, subsequent to the worker nodes 1006obtaining processed information (e.g., based on the dataset source andlogic), the worker nodes 1006 can provide the processed information forstorage in the query acceleration data store 1008 while continuing toprocess the query (e.g., apply the subsequent logic). That is, theworker nodes 1006 can bifurcate the data (e.g., at branch layer 1310),such that the query acceleration data store 1008 can store partialresults while the worker nodes 1006 service the query and provide thecompleted results to the query coordinator 1004. Optionally, anotherquery may be received that references the partial results in the datastore 1008, and one or more worker nodes 1006 may access the data store1008 to service the other query. For example, the other query may beprocessed at a same time as the above-described example initial query.

Received queries can further indicate multiple datasets stored by thequery acceleration data store 1008. For example, a first query canindicate that first information is to be obtained (e.g., from externaldata source 1018, indexers 206, common storage, and so on) and stored inthe query acceleration data store 1008 as a first dataset. Additionally,a second query can indicate that second information is to obtained andstored in the data store 1008 as a second dataset. Subsequent queriescan then reference the stored first dataset and second dataset, suchthat logic can be applied to both the first and second dataset via rapidaccess to the query acceleration data store 1008.

Furthermore, queries can reference datasets stored by the queryacceleration data store 1008, and also datasets to be obtained fromanother dataset source (e.g., from external data source 1018, indexers206, ingested data buffer, and so on). For particular queries, the dataintake and query system 1001 may be able to provide results (e.g.,search results) from the query acceleration data store 1008 whiledatasets is being obtained from another dataset source. Similarly, thesystem 1001 may be able to provide results from the data store 1008while data obtained from another dataset source is being processed.

As an example, a first query can cause a dataset to be stored in thequery acceleration data store 1008, with the dataset being from anexternal data source 1018 and representing records from a prior timeperiod (e.g., one hour). Subsequently, a second query can reference thestored dataset and further cause newer records to be obtained from theexternal data source (e.g., a subsequent hour). For this second query,particular logic indicated in the second query can enable the dataintake and query system 1001 to provide results to a requesting clientbased on the stored dataset in the query acceleration data store 1008.As an example, the second query can indicate that the system 1001 is tosearch for a particular name. The worker nodes 1006 can obtain storedinformation from the query acceleration data store 1008, and identifyinstances of the particular name.

This access to the query acceleration data store 1008, as describedabove, can be low-latency. For example, the query acceleration datastore 1008 may have a portion of the stored information in low-latencymemory, such as RAM or volatile memory, and the worker nodes 1006 canquickly obtain the information and identify instances of the particularname. These identified instances can then be relayed to the requestingclient. Similarly, the query acceleration data store 1008 may have adifferent portion of the stored information in longer-latency memory,and can similarly identify instances of the particular name to beprovided to the requesting client.

The above-described worker node 1006 interactions with the queryacceleration data store 1008 can occur while information is beingobtained, or processed, from the external data source 1018 referenced bythe second query. In this way, the requesting client can view searchresults, for example search results based on the dataset stored by thequery acceleration data store 1008, while subsequent search results arebeing determined (e.g., search results based on information from adifferent dataset source). Furthermore, and as described above, thedataset being obtained from the other dataset source can be provided tothe query acceleration data store 1008 for storage, for example,provided while the worker nodes 1006 apply logic to determine resultsfrom the obtained dataset.

To increase security of the datasets stored by the query accelerationdata store, access controls can be implemented. For example, eachdataset can be associated with an access control list, and the querycoordinator 1004 can provide an identification of a requesting user tothe worker nodes 1006 and/or query acceleration data store 1008. Forexample, the identification can be an authorization or authenticationtoken associated with the user. The query acceleration data store 1008can then ensure that only authorized users are allowed access to storeddatasets. For example, a user who causes a dataset to be stored in thequery acceleration data store 1008 (e.g., based on a provided query) canbe indicated as being authorized (e.g., in an access control listassociated with the dataset). Optionally, the user can indicate one ormore other users as having access. Optionally, the data intake and querysystem 108 can utilize role-based access controls to allow any userassociated with a particular role to access particular datasets. In thisway, the stored information can be secure while enabling the queryacceleration data store 1008 to service multitudes of users.

5.0. Query Data Flow

FIG. 14 is a data flow diagram illustrating an embodiment ofcommunications between various components within the environment 1000 toprocess and execute a query. At (1), the search head 210 receives andprocesses a query. At (2), the search head 210 communicates the query tothe search process service, which can refer to the search process master1002 and/or query coordinator 1004.

At (3) the search process service processes the query. As described ingreater detail above, as part of processing the query, the querycoordinator 1004 can identify the dataset sources (e.g., external datasources 1018, indexers 206, query acceleration data store 1008, commonstorage, ingested data buffer, etc.) to be accessed, generateinstructions for the dataset sources based on their processingcapabilities or communication protocols, determine the size of thequery, determine the amount of resources to allocate for the query,generate instructions for the nodes 1006 to execute the query, andgenerate tasks for itself to process results from the nodes 1006.

At (4), the query coordinator 1004 communicates the task instructionsfor the query to the worker nodes 1006 and/or the dataset sources 1404.As described above, in some embodiments, the query coordinator 1004 cancommunicate task instructions to the dataset sources 1404. In certainembodiments, the nodes 1006 communicate task instructions to the datasetsources 1404.

At (5), the nodes 1006 and/or dataset sources 1404 process the receivedinstructions. As described in greater detail above, the instructions forthe dataset sources 1404 can include instructions for performing certaintransformations on the data prior to communicating the data to the nodes1006, etc. As described in greater detail above, the instructions forthe nodes 1006 can include instructions on how to access the relevantdata, the number of search phases or layers to be generated, the numberof partitions to be allocated for each search phase or layer, the tasksfor the partitions in the different layer, data routing information toroute data between the nodes 1006 and to the search process service1402, etc. As such, based on the received instructions, the nodes 1006can assign partitions to different layer and begin executing the taskinstructions.

At (6), the nodes 1006 receive the data from the dataset source(s). Asdescribed in greater detail above, the nodes 1006 can receive the datafrom one or more dataset sources 1404 in parallel. In addition, thenodes 1006 can receive the data from a dataset source using one or morepartitions. The data received from the dataset sources 1404 can besemi-processed data based on the processing capabilities of the datasetsource 1404 or it can be unprocessed data from the dataset source 1404.

At (7), the nodes 1006 process the data based on the task instructionsreceived from the query coordinator 1004. As described in greater detailabove, the nodes can process the data using one or more layers, eachhaving one or more partitions assigned thereto. Although not illustratedin FIG. 37, it will be understood that the search process service 1402can monitor the nodes 1006 and dynamically allocate resources based onthe monitoring.

At (8), the nodes 1006 communicate the results of the processing to thequery coordinator 1004 and/or to a dataset destination 1404. In somecases the dataset destination 1404 can be the same as the datasetsource. For example, the nodes 1006 can obtain data from the ingesteddata buffer and then return the results of the processing to a differentsection of the ingested data buffer, or obtain data from the queryacceleration data store 1008 or an external data source 1018 and thenreturn the results of the processing to the query acceleration datastore 1008 or external data source 1018, respectively. However, incertain embodiments, the dataset destination 1404 can be different fromthe dataset source 1404. For example, the nodes 1006 can obtain datafrom the ingested data buffer and then return the results of theprocessing to the query acceleration data store 1008 or an external datasource 1018.

At (9), the search process service 1402 can perform additionalprocessing, and at (10) the results can be communicated to the searchhead 210 for communication to the client device. In some cases, prior tocommunicating the results to the client device, the search head 210 canperform additional processing on the results.

It will be understood that the query data flow can include fewer or moresteps. For example, in some cases, the search process service 1402 doesnot perform any further processing on the results and can simply forwardthe results to the search head 210. In certain embodiments, nodes 1006receive data from multiple dataset sources 1404, etc.

6.0. Query Coordinator Flow

FIG. 15 is a flow diagram illustrative of an embodiment of a routine1500 implemented by the query coordinator 1004 to provide query results.Although described as being implemented by the query coordinator 1004,one skilled in the relevant art will appreciate that the elementsoutlined for routine 1500 can be implemented by one or more computingdevices/components that are associated with the system 1001, such as thesearch head 210, search process master 1001, indexer 206, and/or workernodes 1006. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 1502, the query coordinator 1004 receives a query. As describedin greater detail above, the query coordinator 1004 can receive thequery from the search head 210, search process master 1002, etc. In somecases, the query coordinator 1004 can receive the query from a client404. The query can be in a query language as described in greater detailabove. In some cases, the query received by the query coordinator 1004can correspond to a query received and reviewed by the search head 210.For example, the search head 210 can determine whether the query wassubmitted by an authenticated user and/or review the query to determinethat it is in a proper format for the data intake and query system 1001,has correct semantics and syntax, etc. In some cases, the search head210 can run a daemon to receive search queries, and in some cases, spawna search process, to communicate the received query to and receive theresults from the query coordinator 1004 or search process master 1002

At block 1504, the query coordinator 1004 processes the query. Asdescribed in greater detail above and as will be described in greaterdetail in FIG. 16, processing the query can include any one or anycombination of: identifying relevant dataset sources and destinationsfor the query, obtaining information about the dataset sources anddestinations, determining processing tasks to execute the query,determining available resources for the query, and/or generating a queryprocessing scheme to execute the query based on the information. In someembodiments, as part of generating a query processing scheme, the querycoordinator 1004 allocates multiple layers or search phases ofpartitions to execute the query. Each level of partitions can be given adifferent task in order to execute the query. For example, as describedin greater detail above with reference to FIGS. 12 and 13, one level canbe given the task of interacting with the dataset source and receivingdata from the dataset source, another level can be tasked withprocessing the data received from the dataset source, a third level canbe tasked with collecting results of processing the data, and additionallevels can be tasked with communicating results to differentdestinations, storing the results in one or more dataset destinations,etc. The query coordinator 1004 can allocate as many or as few levels ofpartitions to execute the query.

At block 1506, the query coordinator 1004 distributes the query forexecution. Distributing the query for execution can include any one orany combination of: communicating the query processing scheme to thenodes 1006, monitoring the nodes 1006 during the processing of thequery, or allocating/deallocating resources based on the status of thenodes and the query, and so forth, described in greater herein.

At block 1508, the query coordinator 1004 receives the results. In someembodiments, the query coordinator 1004 receives the results from thenodes 1006. For example, upon completing the query processing scheme, oras a part of it, the nodes 1006 can communicate the results of the queryto the query coordinator 1004. In certain cases, the query coordinator1004 receives the results from the query acceleration data store, orindexers 206, etc. In some cases, the query coordinator 1004 receivesthe results from one or more components of the data intake and querysystem 1001 depending on the dataset sources used in the query.

At block 1510, the query coordinator 1004 processes the results. Asdescribed in greater detail above, in some cases, the results of a querycannot be finalized by the nodes 1006. For example, in some cases, allof the data must be gathered before the results can be determined. As anon-limiting example, for some cursored searches, the query coordinator1004, a result cannot be determined until all relevant data has beencollected by the worker nodes. In such cases, the query coordinator 1004can receive the results from the worker nodes 1006, and then collate theresults.

At block 1512, the query coordinator 1004 communicates the results. Insome embodiments, the query coordinator 1004 communicates the results tothe search head 210, such as a search process generated by the search tohandle the query. In certain cases, the query coordinator 1004communicates the results to the search process master 1002 or clientdevice 404, etc.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 1500. In some cases, one or more blocks can beomitted. For example, in certain embodiments, the results received fromnodes 1006 can be in a form that does not require any additionalprocessing by the query coordinator 1004. In such embodiments, the querycoordinator 1004 can communicate the results without additionalprocessing. As another example, the routine 1500 can include monitoringnodes during execution of the query or query processing scheme,allocating or deallocating resources during the execution of the query,etc. Similarly, routine 1500 can include reporting completion of thequery to a component, such as the search process master 1002, etc.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 15 can be implemented in a variety oforders. In some cases, the query coordinator 1004 can implement someblocks concurrently or change the order as desired. For example, thequery coordinator 1004 can receive (1508), process (1510), and/orcommunicate results (1512) concurrently or in any order, as desired.

7.0. Query Processing Flow

FIG. 16 is a flow diagram illustrative of an embodiment of a routine1600 implemented by the query coordinator 1004 to process a query.Although described as being implemented by the query coordinator 1004,one skilled in the relevant art will appreciate that the elementsoutlined for routine 1600 can be implemented by one or more computingdevices/components that are associated with the system 1001, such as thesearch head 210, search process master 1001, indexer 206, and/or workernodes 1006. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 1602, the query coordinator 1004 identifies dataset sourcesand/or destinations for the query. In some cases, the query explicitlyidentifies the dataset sources and destinations that are to be used inthe query. For example, the query can include a command indicating thatdata is to be retrieved from the query acceleration data store 1008,ingested data buffer, common storage, indexers, or an external datasource. In certain cases, the query coordinator 1004 parses the query toidentify the dataset sources and destinations that are to be used in thequery. For example, the query may identify the name (or otheridentifier) of the location (e.g., my_index) of the relevant data andthe query coordinator 1004 can use the name or identifier to determinewhether that particular location is associated with the queryacceleration data store 1008, ingested data buffer, common storage,indexers 206, or an external data source 1018.

In some cases, the query coordinator identifies the dataset source basedon timing requirements of the search. For example, in some cases,queries for data that satisfy a timing threshold or are within a timeperiod are handled by indexers or correspond to data in an ingested databuffer, as described herein. In some embodiments, data that does notsatisfy the timing threshold or is outside of the time period are storedin common storage, query acceleration data stores, external datasources, or by indexers. For example, as described in greater detailherein, in some cases, the indexers fill hot buckets with incoming data.Once a hot bucket is filled, it is stored. In some embodiments hotbuckets are searchable and in other embodiments hot buckets are not.Accordingly, in embodiments where hot buckets are searchable, a querythat reflects a time period that includes hot buckets can indicate thatthe dataset source is the indexers, or hot buckets being processed bythe indexers. Similarly, in embodiments where warm buckets are stored bythe indexers, a query that reflects a time period that includes warmbuckets can indicate that the dataset source is the indexers.

In certain embodiments, a query for data that satisfies the timingthreshold or is within the time period can indicate that the ingesteddata buffer is the dataset source. Further, in embodiments, where warmbuckets are stored in a common storage, a query for data that does notsatisfy the timing threshold or is outside of the time period canindicate that the common storage is the dataset source. In someembodiments, the time period can be reflective of the time it takes fordata to be processed by the data intake and query system 1001 and storedin a warm bucket. Thus, a query for data within the time period canindicate that the data has not yet been indexed and stored by theindexers 206 or that the data resides in hot buckets that are stillbeing processed by the indexers 206.

In some embodiments, the query coordinator 1004 identifies the datasetsource based on the architecture of the system 1001. As describedherein, in some architectures, real-time searches or searches for datathat satisfy the timing threshold are handled by indexers. In otherarchitectures, these same types of searches are handled by the nodes1006 in combination with the ingested data buffer. Similarly, in certainarchitectures, historical searches, or searches for data that do notsatisfy the timing threshold are handled by the indexers. In otherarchitectures, these same types of searches are handled by the nodes1006 in combination with the common storage.

At block 1604 the query coordinator 1004 obtains relevant informationabout the dataset sources/destinations. The query coordinator 1004 canobtain the relevant information from a variety of sources, such as theworkload advisor 1010, workload catalog 1012, dataset compensationmodule 1016, the dataset sources/destinations themselves, etc. Forexample, if the dataset source/destination is an external data source,the query coordinator 1004 can obtain relevant information about theexternal dataset source 1018 from the dataset compensation module or bycommunicating with the external data source 1018. Similarly, if thedataset source/destination is an indexer 206, common storage, queryacceleration data store 1008, ingested data buffer, etc., the querycoordinator can obtain relevant information by communicating with thedataset source/destination and/or the workload advisor 1010 or workloadcatalog 1012.

The relevant information can include, but is not limited to, informationto enable the query coordinator 1004 to generate a search scheme withsufficient information to interact with and obtain data from a datasetsource or send data to a dataset destination. For example, the relevantinformation can include information related to the number of partitionssupported by the dataset source/destination, location of compute nodesat the dataset source/destination, computing functionality of thedataset source/destination, commands supported by the datasetsource/destination, physical location of the dataset source/destination,network speed and reliability in communicating with the datasetsource/destination, amount of information stored by the datasetsource/destination, computer language or protocols for communicatingwith the dataset source/destination, summaries or indexes of data storedby the dataset source/destination, data format of data stored by thedataset source/destination, etc.

At block 1606, the query coordinator 1004 determines processingrequirement for the query. In some cases, to determine the processingrequirements, the query coordinator 1004 parses the query. As describedpreviously, the workload catalog 1012 can store information regardingthe various transformations or commands that can be executed on data andthe amount of processing to perform the transformation or command. Insome cases, this information can be based on historical information fromprevious queries executed by the system 1001. For example, the querycoordinator 1004 can determine that a “join” command will havesignificant computational requirements, whereas a “count by” command maynot. Using the information about the transformations included in thequery, the query coordinator can determine the processing requirementsof individual transformations on the data, as well as the processingrequirements of the query.

At block 1608, the query coordinator 1004 determines availableresources. As described in greater detail above, the nodes 1006 caninclude monitoring modules that monitor the performance and utilizationof its processors. In some cases, a monitoring module can be assignedfor each processor on a node. The information about the utilization rateand other scheduling information can be used by the query coordinator1004 to determine the amount of resources available for the query.

At block 1610, the query coordinator 1004 generates a query processingscheme. In some cases, the query coordinator 1004 can use theinformation regarding the dataset sources/destinations, the processingrequirements of the query and/or the available resources to generate thequery processing scheme. As part of generating the query processingscheme, the query coordinator 1004 can generate instructions to beexecuted by the dataset sources/destinations, allocatepartitions/processors for the query, generate instructions for thepartitions/nodes, generate instructions for itself, generate a DAG, etc.

As described in greater detail above, in some embodiments, to generateinstructions for the dataset sources/destinations, the query coordinator1004 can use the information from the dataset compensation module 1016.This information can be used by the query coordinator 1004 to determinewhat processing can be done by an external data source, how to translatethe commands or subqueries for execution to the external dataset source,the number of partitions that can be used to read data from the externaldataset source, etc. Similarly, the query coordinator 1004 can generateinstructions for other dataset sources, such as the indexers, queryacceleration data store, common storage, etc. For example, the querycoordinator 1004 can generate instructions for the ingested data bufferto retain data until it receives an acknowledgment from the querycoordinator that the data from the ingested data buffer has beenreceived and processed.

In addition, as described in greater detail above, to generateinstructions for the processors/partitions, the query coordinator 1004can determine how to break up the processing requirements of the queryinto discrete or individual tasks, determine the number ofpartitions/processors to execute the task, etc. In some cases, thedetermine how to break up the processing requirements of the query intodiscrete or individual tasks, the query coordinator 1004 can parse thequery to its different portions of the query and then determine thetasks to use to execute the different portions.

The query coordinator 1004 can then use this information to generatespecific instructions for the nodes that enable the nodes to execute theindividual tasks, route the results of each task to the next location,and route the results of the query to the proper destination. Theinstructions for the nodes can further include instructions forinteracting with the dataset sources/destinations. In some cases,instructions for the dataset sources can be embedded in the instructionsfor the nodes so that the nodes can communicate the instructions to thedataset sources/destinations. Accordingly, the instructions generated bythe query coordinator 1004 for the nodes can include all of theinformation in order to enable the nodes to handle the various tasks ofthe query and provide the query coordinator with the appropriate data sothat the query coordinator 1004 can finalize the results and communicatethem to the search head 210.

In some cases, the query coordinator 1004 can use network topologyinformation of the machines that will be executing the query to generatethe instructions for the nodes. For example, the query coordinator 1004can use the physical location of the processors that will execute thequery to generate the instructions. As one example, the querycoordinator 1004 can indicate that it is preferred that the processorsassigned to execute the query be located on the same machine or close toeach other.

In some embodiments, the instructions for the nodes can be generated inthe form of a DAG, as described in greater detail above. The DAG caninclude the instructions for the nodes to carry out the processing tasksincluded in the DAG. In some cases, the DAG can include additionalinformation, such as instructions on how to select partitions for thedifferent tasks. For example, the DAG can indicate that it is preferablethat a partition that will be receiving data from another partition beon the same machine, or nearby machine, in order to reduce networktraffic.

In addition to generating instructions for the datasetsources/destinations and the nodes, the query coordinator 1004 cangenerate instructions for itself. In some cases, the instructionsgenerated for itself can depend on the query that is being processed,the capabilities of the nodes 1006, and the results expected from thenodes. For example, in some cases, the type of query requested mayrequire the query coordinator 1004 to perform more or less processing.For example, a cursored search may require more processing by the querycoordinator 1004 than a batch search. Accordingly, the query coordinator1004 can generate tasks or instructions for itself based on the queryrequested.

In addition, if the nodes 1006 are unable to perform certain tasks onthe data, then the query coordinator 1004 can assign those tasks toitself and generate instructions for itself based on those tasks.Similarly, based on the form of the data that the query coordinator 1004is expected to receive, it can generate instructions for itself in orderto finalize the results for reporting.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 1600. In some cases, one or more blocks can beomitted. Furthermore, it will be understood that the various blocksdescribed herein with reference to FIG. 16 can be implemented in avariety of orders. In some cases, the query coordinator 1004 canimplement some blocks concurrently or change the order as desired. Forexample, the query coordinator 1004 can obtain information about thedataset sources/destinations (3904), determine processing requirements(3906), and determine available resources (3908) concurrently or in anyorder, as desired.

8.0. Common Storage Architecture

As discussed above, indexers 206 may in some embodiments operate both toingest information into a data intake and query system 1001, and tosearch that information in response to queries from client devices 404.The use of an indexer 206 to both ingest and search information may bebeneficial, for example, because indexers 206 may have ready access toinformation that they have ingested, and thus be enabled to quicklyaccess that information for searching purposes. However, use of anindexer 206 to both ingest and search information may not be desirablein all instances. As an illustrative example, consider an instance inwhich information within the system 1001 is organized into buckets, andeach indexer 206 is responsible for maintaining buckets within a datastore 208 corresponding to the indexer 206. Illustratively, a set of 10indexers 206 may maintain 100 buckets, distributed evenly across tendata stores 208 (each of which is managed by a corresponding indexer206). Information may be distributed throughout the buckets according toa load-balancing mechanism used to distribute information to theindexers 206 during data ingestion. In an idealized scenario,information responsive to a query would be spread across the 100buckets, such that each indexer 206 may search their corresponding 10buckets in parallel, and provide search results to a search head 210.However, it is expected that this idealized scenario may not alwaysoccur, and that there will be at least some instances in whichinformation responsive to a query is unevenly distributed across datastores 208. As an extreme example, consider a query in which responsiveinformation exists within 10 buckets, all of which are included in asingle data store 208 associated with a single indexer 206. In such aninstance, a bottleneck may be created at the single indexer 206, and theeffects of parallelized searching across the indexers 206 may beminimal. To increase the speed of operation of search queries in suchcases, it may therefore be desirable to configure the data intake andquery system 1001 such that parallelized searching of buckets may occurindependently of the operation of indexers 206.

Another potential disadvantage in utilizing an indexer 206 to bothingest and search data is that computing resources of the indexers 206may be split among those two tasks. Thus, ingestion speed may decreaseas resources are used to search data, or vice versa. It may further bedesirable to separate ingestion and search functionality, such thatcomputing resources available to either task may be scaled ordistributed independently.

One example of a configuration of the data intake and query system 1001that enables parallelized searching of buckets independently of theoperation of indexers 206 is shown in FIG. 17. The embodiment of system1001 that is shown in FIG. 17 substantially corresponds to embodiment ofthe system 1001 as shown in FIG. 10, and thus corresponding elements ofthe system 1001 will not be re-described. However, unlike the embodimentas shown in FIG. 10, where individual indexers 206 are assigned tomaintain individual data stores 208, the embodiment of FIG. 17 includesa common storage 1702. Common storage 1702 may correspond to any datastorage system accessible to each of the indexers 206. For example,common storage 1702 may correspond to a storage area network (SAN),network attached storage (NAS), other network-accessible storage system(e.g., a ho33sted storage system, which may also be referred to as“cloud” storage), or combination thereof. The common storage 1702 mayinclude, for example, hard disk drives (HDDs), solid state storagedevices (SSDs), or other substantially persistent or non-transitorymedia. Data stores 208 within common storage 1702 may correspond tophysical data storage devices (e.g., an individual HDD) or a logicalstorage device, such as a grouping of physical data storage devices or avirtualized storage device hosted by an underlying physical storagedevice. In one embodiment, common storage 1702 may be multi-tiered, witheach tier providing more rapid access to information stored in thattier. For example, a first tier of the common storage 1702 may bephysically co-located with indexers 206 and provide rapid access toinformation of the first tier, while a second tier may be located in adifferent physical location (e.g., in a hosted or “cloud” computingenvironment) and provide less rapid access to information of the secondtier. Distribution of data between tiers may be controlled by any numberof algorithms or mechanisms. In one embodiment, a first tier may includedata generated or including timestamps within a threshold period of time(e.g., the past seven days), while a second tier or subsequent tiersincludes data older than that time period. In another embodiment, afirst tier may include a threshold amount (e.g., n terabytes) orrecently accessed data, while a second tier stores the remaining lessrecently accessed data. In one embodiment, data within the data stores208 is grouped into buckets, each of which is commonly accessible to theindexers 206. The size of each bucket may be selected according to thecomputational resources of the common storage 1702 or the data intakeand query system 1001 overall. For example, the size of each bucket maybe selected to enable an individual bucket to be relatively quicklytransmitted via a network, without introducing excessive additional datastorage requirements due to metadata or other overhead associated withan individual bucket. In one embodiment, each bucket is 750 megabytes insize.

The indexers 206 may operate to communicate with common storage 1702 andto generate buckets during ingestion of data. Data ingestion may besimilar to operations described above. For example, information may beprovided to the indexers 206 by forwarders 204, after which theinformation is processed and stored into buckets. However, unlike someembodiments described above, the buckets may be stored in common storage1702, rather than in a data store 208 maintained by an individualindexer 206. Thus, the common storage 1702 can render information of thedata intake and query system 1001 commonly accessible to elements ofthat system 1001. As will be described below, such common storage 1702can beneficial enable parallelized searching of buckets to occurindependently of the operation of indexers 206.

As noted above, it may be beneficial in some instances to separatewithin the data intake and query system 1001 functionalities ofingesting data and searching for data. As such, in the illustrativeconfiguration of FIG. 17, worker nodes 1006 may be enabled to search fordata stored within common storage 1702. The nodes 1006 may therefore becommunicatively attached (e.g., via a communication network) with thecommon storage 1702, and be enabled to access buckets within the commonstorage 1702. The nodes 1006 may search for data within buckets in amanner similar to how searching may occur at the indexers 206, asdiscussed in more detail above. However, because nodes 1006 in someinstances are not statically assigned to individual data stores 208 (andthus to buckets within such a data store 208), the buckets searched byan individual node 1006 may be selected dynamically, to increase theparallelization with which the buckets can be searched. For example,using the example provided above, consider again an instance whereinformation is stored within 100 buckets, and a query is received at thedata intake and query system 1001 for information within 10 suchbuckets. Unlike the example above (in which only indexers 206 alreadyassociated with those 10 buckets could be used to conduct a search), the10 buckets holding relevant information may be dynamically distributedacross worker nodes 1006. Thus, if 10 worker nodes 1006 are available toprocess a query, each worker node 1006 may be assigned to retrieve andsearch within 1 bucket, greatly increasing parallelization when comparedto the low-parallelization scenario discussed above (e.g., where asingle indexer 206 is required to search all 10 buckets). Moreover,because searching occurs at the worker nodes 1006 rather than atindexers 206, computing resources can be allocated independently tosearching operations. For example, worker nodes 1006 may be executed bya separate processor or computing device than indexers 206, enablingcomputing resources available to worker nodes 1006 to scaleindependently of resources available to indexers 206.

Operation of the data intake and query system 1001 to utilize workernodes 1006 to search for information within common storage 1702 will nowbe described. As discussed above, a query can be received at the searchhead 210, processed at the search process master 1002, and passed to aquery coordinator 1004 for execution. The query coordinator 1004 maygenerate a DAG corresponding to the query, in order to determinesequences of search phases within the query. The query coordinator 1004may further determine based on the query whether each branch of the DAGrequires searching of data within the common storage 1702 (e.g., asopposed to data within external storage, such as remote systems 414 and416).

It will be assumed for the purposes of described that at least onebranch of the DAG requires searching of data within the common storage1702, and as such, description will be provided for execution of such abranch. While interactions are described for executing a single branchof a DAG, these interactions may be repeated (potentially concurrentlyor in parallel) for each branch of a DAG that requires searching of datawithin the common storage 1702. As discussed above with reference toFIG. 13, executing a search representing a branch of a DAG can include anumber of phases, such as an intake phase 1304, processing phase 1306,and collector phase 1308. It is therefore illustrative to discussexecution of a branch of a DAG that requires searching of the commonstorage 1702 with reference to such phases. As also discussed above,each phase may be carried out by a number of partitions, each of whichmay correspond to a worker node 1006 (e.g., a specific worker node 1006,processor within the worker node 1006, execution environment within aworker node 1006, such as a virtualized computing device orsoftware-based container, etc.).

When a branch requires searching within common storage 1702, the querycoordinator 1004 can select a partition (e.g., a processor within aworker node 1006) at random or according to a load-balancing algorithmto gather metadata regarding the information within the common storage1702, for use in dynamically assigning partitions (each implemented by aworker node 1006) to implement an intake phase 1304. Metadata isdiscussed in more detail above, but may include, for example, dataidentifying a host, a source, and a source type related to a bucket ofdata. Metadata may further indicate a range of timestamps of informationwithin a bucket. The metadata can then be compared against a query todetermine a subset of buckets within the common storage 1702 that maycontain information relevant to a query. For example, where a queryspecifies a desired time range, host, source, source type, orcombination thereof, only buckets in the common storage 1702 thatsatisfy those specified parameters may be considered relevant to thequery. In one embodiment, the subset of buckets is determined by theassigned partition, and returned to the query coordinator 1004. Inanother embodiment, the metadata retrieved by a partition is returned tothe query coordinator 1004 and used by the query coordinator 1004 todetermine the subset of buckets.

Thereafter, the query coordinator 1004 can dynamically assign partitionsto intake individual buckets within the determined subset of buckets. Inone embodiment, the query coordinator 1004 attempts to maximizeparallelization of the intake phase 1304, by attempting to intake thesubset of buckets with a number of partitions equal to the number ofbuckets in the subset (e.g., resulting in a one-to-one mapping ofbuckets in the subset to partitions). However, such parallelization maynot be feasible or desirable, for example, where the total number ofpartitions is less than the number of buckets within the determinedsubset, where some partitions are processing other queries, or wheresome partitions should be left in reserve to process other queries.Accordingly, the query coordinator 1004 may interact with the workloadadvisor 1010 to determine a number of partitions that are to be utilizedto conduct the intake phase 1304 of the query. Illustratively, the querycoordinator 1004 may initially request a one-to-one correspondencebetween buckets and partitions, and the workload advisor 1010 may reducethe number of partitions used for the intake phase 1304 of the query,resulting in a 2-to-1, 3-to-1, or n-to-1 correspondence between bucketsand partitions. Operation of the workload advisor 1010 is described inmore detail above.

The query coordinator 1004 can then assign the partitions (e.g., thosepartitions identified by interaction with the workload advisor 1010) tointake the buckets previously identified as potentially containingrelevant information (e.g., based on metadata of the buckets). In oneembodiment, the query coordinator 1004 may assign all buckets as asingle operation. For example, where 10 buckets are to be searched by 5partitions, the query coordinator 1004 may assign 2 buckets to a firstpartitions, two buckets to a second partitions, etc. In anotherembodiment, the query coordinator 1004 may assign buckets iteratively.For example, where 10 buckets are to be searched by 5 partitions, thequery coordinator 1004 may initially assign five buckets (e.g., onebuckets to each partition), and assign additional buckets to eachpartition as the respective partitions complete intake of previouslyassigned buckets.

In some instances, buckets may be assigned to partitions randomly, or ina simple sequence (e.g., a first partitions is assigned a first bucket,a second partitions is assigned a second bucket, etc.). In otherinstances, the query coordinator 1004 may assign buckets to partitionsbased on buckets previously assigned to a partitions, in a prior orcurrent search. Illustratively, in some embodiments each worker node1006 may be associated with a local cache of information (e.g., inmemory of the partitions, such as random access memory [“RAM”] ordisk-based cache). Each worker node 1006 may store copies of one or morebuckets from the common storage 1702 within the local cache, such thatthe buckets may be more rapidly searched by partitions implemented onthe worker node 1006. The query coordinator 1004 may maintain orretrieve from worker nodes 1006 information identifying, for eachrelevant node 1006, what buckets are copied within local cache of therespective nodes 1006. Where a partition assigned to execute a search isimplemented by a worker node 1006 that has within its local cache a copyof a bucket determined to be potentially relevant to the search, thatpartition may be preferentially assigned to search that locally-cachedbucket. In some instances, local cache information can further be usedto determine the partitions to be used to conduct a search. For example,partitions corresponding to worker nodes 1006 that have locally-cachedcopies of buckets potentially relevant to a search may be preferentiallyselected by the query coordinator 1004 or workload advisor 1010 toexecute the intake phase 1304 of a search. In some instances, the querycoordinator 1004 or other component of the system 1001 (e.g., the searchprocess master 1002) may instruct worker nodes 1006 to retrieve andlocally cache copies of various buckets from the common storage 1702,independently of processing queries. In one embodiment, the system 1001is configured such that each bucket from the common storage 1702 islocally cached on at least one worker node 1006. In another embodiment,the system 1001 is configured such that at least one bucket from thecommon storage 1702 is locally cached on at least two worker nodes 1006.Caching a bucket on at least two worker nodes 1006 may be beneficial,for example, in instances where different queries both require searchingthe bucket (e.g., because the at least two worker nodes 3006 may processtheir respective local copies in parallel). In still other embodiments,the system 1001 is configured such that all buckets from the commonstorage 1702 are locally cached on at least a given number n of workernodes 1006, wherein n is defined by a replication factor on the system1001. For example, a replication factor of 5 may be established toensure that 5 searches of buckets can be executed concurrently by 5different worker nodes 1006, each of which has locally cached a copy ofa given bucket potentially relevant to the searches.

In some embodiments, buckets may further be assigned to partitions toassist with time ordering of search results. For example, where a searchrequests time ordering of results, the query coordinator 1004 mayattempt to assign buckets with overlapping time ranges to the samepartition, such that information within the buckets can be sorted at thepartition. Where the buckets assigned to different partitions arenon-overlapping in time, the query coordinator 1004 may sort informationfrom different partitions according to an absolute ordering of thebuckets processed by the different partitions. That is, if alltimestamps in all buckets processed by a first worker node 1006 occurprior to all timestamps in all buckets processed by a second worker node1006, query coordinator 1004 can quickly determine (e.g., withoutreferencing timestamps of information) that all information identifiedby the first worker node 1006 in response to a search occurs in timeprior to information identified by the second worker node 1006 inresponse to the search. Thus, assigning buckets with overlapping timeranges to the same partition can reduce computing resources needed totime-order results.

In still more embodiments, partitions may be assigned based on overlapsof computing resources of the partitions. For example, where a partitionis required to retrieve a bucket from common storage 1702 (e.g., where alocal cached copy of the bucket does not exist on the worker node 1006implementing the partition), such retrieval may use a relatively highamount of network bandwidth or disk read/write bandwidth on the workernode 1006 implementing the partition. Thus, assigning a second partitionof the same worker node 1006 might be expected to strain or exceed thenetwork or disk read/write bandwidth of the worker node 1006. For thisreason, it may be preferential to assign buckets to partitions such thattwo partitions within a common worker node 1006 are not both required toretrieve buckets from the common storage 1702. Illustratively, it may bepreferential to evenly assign all buckets containing potentiallyrelevant information among the different worker nodes 1006 used toimplement the intake phase 1304. For similar reasons, where a givenworker node 1006 has within its local cache two buckets that potentiallyinclude relevant information, it may be preferential to assign both suchbuckets to different partitions implemented by the same worker node1006, such that both buckets can be search in parallel on the workernode 1006 by the respective partitions. In some instances, commonalityof computing resources between partitions can further be used todetermine the partitions to be used to conduct an intake phase 1304. Forexample, the query coordinator 1004 may preferentially select partitionsthat are implemented by different worker nodes 1006 (e.g., in order tomaximize network or disk read/write bandwidth) to implement an intakephase 1304. However, where a worker node 1006 has locally cachedmultiple buckets with information potentially relevant to the search,the query coordinator 1004 may preferentially multiple partitions onthat worker node 1006 (e.g., up to a number of partitions equal to thenumber of potentially-relevant buckets stored at the worker node 1006).

The above mechanisms for assigning buckets to partitions may be combinedbased on priorities of each potential outcome. For example, the querycoordinator 1004 may give an initial priority to distributing assignedpartitions across a maximum number of different worker nodes 1006, but ahigher priority to assigning partitions to process buckets withoverlapping timestamps. The query coordinator 1004 may give yet a higherpriority to assigning partitions to process buckets that have beenlocally cached. The query coordinator 1004 may still further give higherpriority to ensuring that each partition is searching at least onebucket for information responsive to a query at any given time. Thus,the query coordinator 1004 can dynamically alter the assignment ofbuckets to partitions to increase the parallelization of a search, andto increase the speed and efficiency with which the search is executed.

When searching for information within the common storage 1702, theintake phase 1304 may be carried out according to bucket-to-partitionmapping discussed above, as determined by the query coordinator 1004.Specifically, after assigning at least one bucket to each partition tobe used during the intake phase 1304, each partition may begin toretrieve its assigned bucket. Retrieval may include, for example,downloading the bucket from the common storage 1702, or locating a copyof the bucket in a local cache of a worker node 1006 implementing thepartition. Thereafter, each partition may conduct an initial search ofthe bucket for information responsive to a query. The initial search mayinclude processing that is expected to be disk or network intensive,rather than processing (e.g., CPU) intensive. For example, the initialsearch may include accessing the bucket, which may include decompressingthe bucket from a compressed format, and accessing an index file storedwithin the bucket. The initial search may further include referencingthe index or other information (e.g., metadata within the bucket) tolocate one or more portions (e.g., records or individual files) of thebucket that potentially contain information relevant to the search.

Thereafter, the search proceeds to the processing phase 1306, where theportions of buckets identified during the intake phase 1304 are searchedto locate information responsive to the search. Illustratively, thesearching that occurs during the processing phase 1306 may be predictedto be more processor (e.g., CPU) intensive than that which occurredduring the intake phase 1304. As such, the number of partitions used toconduct the processing phase 1306 may vary from that of the intake phase1304. For example, during or after the conclusion of the intake phase1304, each partition implementing that phase 1304 may communicate to thequery coordinator 1004 information regarding the portions identified aspotentially containing information relevant to the query (e.g., thenumber, size, or formatting of portions, etc.). The query coordinator1004 may thereafter determine from that information (e.g., based oninteractions with the workload advisor 1010) the partitions to be usedto conduct the processing phase 1306. In other embodiments, the querycoordinator 1004 may select partitions to be used to conduct theprocessing phase 1306 prior to implementation of the intake phase 1304(e.g., contemporaneously with selecting partitions to conduct the intakephase 1304). The partitions selected for conducting the processing phase1306 may include one or more partitions that previously conducted theintake phase 1304. However, because the processing phase 1306 may beexpected to be more resource intensive than the intake phase 1304 (e.g.,with respect to use of processing cycles), the number of partitionsselected for conducting the processing phase 1306 may exceed the numberof partitions that previously conducted the intake phase 1304. Tominimize network communications, the additional partitions selected toconduct the processing phase 1306 may be preferentially selected to becollocated on a worker node 1006 with a partition that previouslyconducted the intake phase 1304, such that portions of buckets to beprocessed by the additional partitions can be received from a partitionon that worker node 1006, rather than being transmitted across anetwork.

At the processing phase 1306, the partitions may parse the portions ofbuckets located during the intake phase 1304 in order to identifyinformation relative to a search. For example, the may parse theportions of buckets (e.g., individual files or records) to identifyspecific lines or segments that contain values specified within thesearch, such as one or more error types desired to be located during thesearch. Where the search is conducted according to map-reducetechniques, the processing phase 1306 can correspond to implementing amap function. Where the search requires that results be time-ordered,the processing phase 1306 may further include sorting results at eachpartition into a time-ordering.

The remainder of the search may be executed in phases according to theDAG determined by the query coordinator 1004. For example, where thebranch of the DAG currently being processed includes a collection node,the search may proceed to a collector phase 1308. The collector phase1308 may be executed by one or more partitions selected by the querycoordinator 1004 (e.g., based on the information identified during theprocessing phase 1306), and operate to aggregate information identifiedduring the processing phase 1306 (e.g., according to a reduce function).Where the processing phase 1306 represents a top-node of a branch of theDAG being executed, the information located by each partition during theprocessing phase 1306 may be transmitted to the query coordinator 1004,where any additional nodes of the DAG are completed, and search resultsare transmitted to a data destination 1316. These additional phases maybe implemented in a similar manner as described above, and they aretherefore not discussed in detail with respect to searches against acommon storage 1702.

As will be appreciated in view of the above description, the use of acommon storage 1702 can provide many advantages within the data intakeand query system 1001. Specifically, use of a common storage 1702 canenable the system 1001 to decouple functionality of data ingestion, asimplemented by indexers 206, with functionality of searching, asimplemented by partitions of worker nodes 1006. Moreover, becausebuckets containing data are accessible by each worker node 1006, a querycoordinator 1004 can dynamically allocate partitions to buckets at thetime of a search in order to maximize parallelization. Thus, use of acommon storage 1702 can substantially improve the speed and efficiencyof operation of the system 1001.

9.0. Ingested Data Buffer Architecture

One embodiment of the system 1001 that enables worker nodes 1006 tosearch not-yet-indexed information is shown in FIG. 18. Searching ofnot-yet-indexed information (e.g., prior to processing of theinformation by an indexer 206) may be beneficial, for example, whereinformation is desired on a continuous or streaming basis. For example,a client device 404 a may desire to establish a long-running (e.g.,until manually halted) search of data received at the data intake andquery system 1001, such that the client is quickly notified onoccurrence of specific types of information within the data, such aserrors within machine records. Thus, it may be desirable to conduct thesearch against the data as it enters intake and query system 1001,rather than waiting for the data to be processed by the indexers 206 andsaved into a data store 208.

The embodiment of FIG. 18 is similar to that of FIG. 17, andcorresponding elements will not be re-described. However, unlike theembodiment of FIG. 17, the embodiment of FIG. 18 includes an ingesteddata buffer 1802. The ingested data buffer 1802 of FIG. 18 operates toreceive information obtained by the forwarders 204 from the data sources202, and make such information available for searching to both indexers206 and worker nodes 1006. As such, the ingested data buffer 1802 mayrepresent a computing device or computing system in communication withboth the indexers 206 and the worker nodes 1006 via a communicationnetwork.

In one embodiment, the ingested data buffer 1802 operates according to apublish-subscribe (“pub-sub”) messaging model. For example, each datasource 202 may be represented as one or more “topics” within a pub-submodel, and new information at the data source may be represented as a“message” within the pub-sub model. Elements of the system 1001,including indexers 206 and worker nodes 1006 (or partitions withinworker nodes 1006) may subscribe to a topic representing desiredinformation (e.g., information of a particular data source 202) toreceive messages within the topic. Thus, an element subscribed to arelevant topic will be notified of new data categorized under the topicwithin the ingested data buffer 1802. A variety of implementations ofthe pub-sub messaging model are known in the art, and may be usablewithin the ingested data buffer 1802. As will be appreciated based onthe description below, use of a pub-sub messaging model can provide manybenefits to the system 1001, including the ability to search dataquickly after the data is received at the ingested data buffer 1802(relative to waiting of the data to be processed by an indexer 206)while maintaining or increasing data resiliency.

In embodiments that utilize an ingested data buffer 1802, operation ofthe indexer 206 may be modified to receive information from the buffer1802. Specifically, each indexer 206 may be configured to subscribe toone or more topics on the ingested data buffer 1802 and to thereafterprocess the information in a manner similarly to as described above withrespect to other embodiments of the system. After data representing amessage has been processed by an indexer 206, the indexer 206 can sendan acknowledgement of the message to the ingested data buffer 1802. Inaccordance with the pub-sub messaging model, the ingested data buffer1802 can delete a message once acknowledgements have been received fromall subscribers (which may include, for example, a single indexer 206configured to process the message). Thereafter, operation of the system1001 to store the information processed by the indexer 206 and enablesearching of such information is similar to embodiments described above(e.g., with reference to FIGS. 10 and 17, etc.).

As discussed above, the ingested data buffer 1802 is also incommunication with the worker nodes 1006. As such, the data intake andquery system 1001 can be configured to utilize the worker nodes 1006 tosearch data from the ingested data buffer 1802 directly, rather thanwaiting for the data to be processed by the indexers 206. As discussedabove, a query can be received at the search head 210, processed at thesearch process master 1002, and passed to a query coordinator 1004 forexecution. The query coordinator 1004 may generate a DAG correspondingto the query, in order to determine sequences of search phases withinthe query. The query coordinator 1004 may further determine based on thequery whether any branch of the DAG requires searching of data withinthe ingested data buffer 1802. For example, the query coordinator 1004may determine that at least one branch of the query requires searchingof data within the ingested data buffer 1802 by identifying, within thequery, a topic of the ingested data buffer 1802 for searching. It willbe assumed for the purposes of described that at least one branch of theDAG requires searching of data within the ingested data buffer 1802, andas such, description will be provided for execution of such a branch.While interactions are described for executing a single branch of a DAG,these interactions may be repeated (potentially concurrently or inparallel) for each branch of a DAG that requires searching of datawithin the ingested data buffer 1802. As discussed above with referenceto FIG. 13, executing a search representing a branch of a DAG caninclude a number of phases, such as an intake phase 1304, processingphase 1306, and collector phase 1308. It is therefore illustrative todiscuss execution of a branch of a DAG that requires searching of thecommon storage 1702 with reference to such phases. As also discussedabove, each phase may be carried out by a number of partitions, each ofwhich may correspond to a worker node 1006 (e.g., a specific worker node1006, processor within the worker node 1006, execution environmentwithin a worker node 1006, etc.). Particularly in the case of streamingor continuous searching, different instances of the phases may becarried out at least partly concurrently. For example, the processingphase 1306 may occur with respect to a first set of information whilethe intake phase 1304 occurs with respect to a second set ofinformation, etc. Thus, while the phases will be discussed in sequencebelow, it should be appreciated that this sequence can occur multipletimes with respect to a single query (e.g., as new data enters thesystem 1001), and each sequence may occur at least partiallyconcurrently with one or more other sequences. Moreover, because theingested data buffer 1802 can be configured to make messages availableto any number of subscribers, the sequence discussed below may occurwith respect to multiple different searches, potentially concurrently.Thus, the architecture of FIG. 18 provides a highly scalable, highlyresilient, high availability architecture for searching informationreceived at the system 1001.

When a branch requires searching within ingested data buffer 1802, thequery coordinator 1004 can select a partition (e.g., a processor withina worker node 1006) at random or according to a load-balancing algorithmto gather metadata regarding the topic specified within the query fromthe ingested data buffer 1802. Metadata regarding a topic may include,for example, a number of message queues within the ingested data buffer1802 corresponding to the topic. Each message queue can represent acollection of messages published to the topic, which may be time-ordered(e.g., according to a time that the message was received at the ingesteddata buffer 1802). In some instances, the ingested data buffer 1802 mayimplement a single message queue for a topic. In other instances, theingested data buffer 1802 may implement multiple message queues (e.g.,across multiple computing devices) to aid in load-balancing operation ofthe ingested data buffer 1802 with respect to the topic. The selectedpartition can determine the number of message queues maintained at theingested data buffer 1802 for a topic, and return this information tothe query coordinator.

Thereafter, the query coordinator 1004 can dynamically assign partitionsto conduct an intake phase 1304, by retrieving individual message queuesof the topic within the ingested data buffer 1802. In one embodiment,the query coordinator 1004 attempts to maximize parallelization of theintake phase 1304, by attempting to retrieve messages from the messagequeues with a number of partitions equal to the number of message queuesfor the topic maintained at the ingested data buffer 1802 (e.g.,resulting in a one-to-one mapping of message queues in the topic topartitions). However, such parallelization may not be feasible ordesirable, for example, where the total number of partitions is lessthan the number of message queues, where some partitions are processingother queries, or where some partitions should be left in reserve toprocess other queries. Accordingly, the query coordinator 1004 mayinteract with the workload advisor 1010 to determine a number ofpartitions that are to be utilized to intake messages from the messagequeues during the intake phase 1304. Illustratively, the querycoordinator 1004 may initially request a one-to-one correspondencebetween message queues and partitions, and the workload advisor 1010 mayreduce the number of partitions used to read the message queues,resulting in a 2-to-1, 3-to-1, or n-to-1 correspondence between messagequeues and partitions. Operation of the workload advisor 1010 isdescribed in more detail above. When a greater than 1-to-1correspondence exists between queues and partitions (e.g., 2-to-1,3-to-1, etc.), the message queues may be evenly assigned among differentworker nodes 1006 used to implement the intake phase 1304, to maximizenetwork or read/write bandwidth available to partitions conducting theintake phase 1304.

During the intake phase 1304, each partition used during the intakephase 1304 can subscribe to those message queues assigned to thepartition. Illustratively, where partitions are assigned in a 1-to-1correspondence with message queues for a topic in the ingested databuffer 1802, each partition may subscribe to one corresponding messagequeue. Thereafter, in accordance with the pub-sub messaging model, thepartition can receive from the ingested data buffer 1802 messagespublishes within those respective message queues. However, to ensuremessage resiliency, a partition may decline to acknowledge the messagesuntil such messages have been fully searched, and results of the searchhave been provided to a data destination (as will be described in moredetail below).

In some embodiments, a partition may, during the intake phase 1304 actas an aggregator of messages published to a respective message queue ofthe ingested data buffer 1802, to define a collection of data to beprocessed during an instance of the processing phase 1306. For example,the partition may collect messages corresponding to a given time-window(such as a 30 second time window, 1 minute time window, etc.), andbundle the messages together for further processing during a processingphase 1306 of the search. In one instance, the time window may be set toa duration lower than a typical delay needed for an indexer 206 toprocess information from the ingested data buffer 1802 and place theprocessed information into a data store 208 (as, if a time-windowgreater than this delay were used, a search could instead be conductedagainst the data stores 208). The time window may further be set basedon an expected variance between timestamps in received information andthe time at which the information is received at the ingested databuffer 1802. For example, it is possible the information arrives at theingested data buffer 1802 in an out-of-order manner (e.g., such thatinformation with a later timestamp is received prior to information withan earlier timestamp). If the actual delay in receiving out-of-orderinformation (e.g., the delay between when information is actuallyreceived and when it should have been received to maintain propertime-ordering) exceeds the time window, it is possible that the delayedinformation will be processed during a later instance of the processingphase 1306 (e.g., with a subsequent bundle of messages), and as such,results derived from the delayed information may be deliveredout-of-order to a data destination. Thus, a longer time-window canassist in maintaining order of search results. In some instances, theingested data buffer 1802 may guarantee time ordering of results withineach message queue (though potentially not across message queues), andthus, modification of a time window in order to maintain ordering ofresults may not be required. In still more embodiments, the time-windowmay further be set based on computing resources available at the workernodes 1006. For example, a longer time window may reduce computingresources used by a partition, by enabling a larger collection ofmessages to be processed at a single instance of the processing phase1306. However, the longer time window may also delay how quickly aninitial set of results are delivered to a data destination. Thus, thespecific time-window may vary across embodiments of the presentdisclosure.

While embodiments are described herein with reference to a collection ofmessages defined according to a time-window, other embodiments of thepresent disclosure may utilize additional or alternative collectiontechniques. For example, a partition may be configured to include nomore than a threshold number of messages or a threshold amount of datain a collection, regardless of a time-window for collection. As anotherexample, a partition may be configured during the intake phase 1304 notto aggregate messages, but rather to pass each message to a processingphase 1306 immediately or substantially immediately. Thus, embodimentsrelated to time-windowing of messages are illustrative in nature.

In some embodiments, the partitions, during the intake phase 1304 mayfurther conduct coarse filtering on the messages received during a giventime-window, in order to identify any messages not relevant to a givenquery. Illustratively, the coarse filtering may include comparison ofmetadata regarding the message (e.g., a source, source type, or hostrelated to the message), in order to determine whether the metadataindicates that the message is irrelevant to the query. If so, such amessage may be removed from the collection prior to the search processproceeding to the processing phase 1306. In one embodiment, the coarsefiltering does not include searching for or processing the actualcontent of a message, as such processing may be predicted to berelatively computing resource intensive.

After generating a collection of messages from a respective messagequeue, the search can proceed to the processing phase 1306, where one ormore partitions are utilize to search the messages for informationrelevant to the search query. Illustratively, the searching that occursduring the processing phase 1306 may be predicted to be more processor(e.g., CPU) intensive than that which occurred during the intake phase1304. As such, the number of partitions used to conduct the processingphase 1306 may vary from that of the intake phase 1304. For example,during or after the conclusion of the intake phase 1304, each partitionimplementing that phase 1304 may communicate to the query coordinator1004 information regarding the collections of messages received during agiven time-window (e.g., the number, size, or formatting of messages,etc.). The query coordinator 1004 may thereafter determine from thatinformation (e.g., based on interactions with the workload advisor 1010)the partitions to be used to conduct the processing phase 1306. In otherembodiments, the query coordinator 1004 may select partitions to be usedto conduct the processing phase 1306 prior to implementation of theintake phase 1304 (e.g., contemporaneously with selecting partitions toconduct the intake phase 1304). The partitions selected for conductingthe processing phase 1306 may include one or more partitions thatpreviously conducted the intake phase 1304. However, because theprocessing phase 1306 may be expected to be more resource intensive thanthe intake phase 1304 (e.g., with respect to use of processing cycles),the number of partitions selected for conducting the processing phase1306 may exceed the number of partitions that previously conducted theintake phase 1304. To minimize network communications, the additionalpartitions selected to conduct the processing phase 1306 may bepreferentially selected to be collocated on a worker node 1006 with apartition that previously conducted the intake phase 1304, such thatportions of buckets to be processed by the additional partitions can bereceived from a partition on that worker node 1006, rather than beingtransmitted across a network.

At the processing phase 1306, the partitions may parse the collectionsof messages generated during the intake phase 1304 in order to identifyinformation relative to a search. For example, the may parse individualmessages to identify specific lines or segments that contain valuesspecified within the search, such as one or more error types desired tobe located during the search. Where the search is conducted according tomap-reduce techniques, the processing phase 1306 can correspond toimplementing a map function. Where the search requires that results betime-ordered, the processing phase 1306 may further include sortingresults at each partition into a time-ordering.

The remainder of the search may be executed in phases according to theDAG determined by the query coordinator 1004. For example, where thebranch of the DAG currently being processed includes a collection node,the search may proceed to a collector phase 1308. The collector phase1308 may be executed by one or more partitions selected by the querycoordinator 1004 (e.g., based on the information identified during theprocessing phase 1306), and operate to aggregate information identifiedduring the processing phase 1306 (e.g., according to a reduce function).Where the processing phase 1306 represents a top-node of a branch of theDAG being executed, the information located by each partition during theprocessing phase 1306 may be transmitted to the query coordinator 1004,where any additional nodes of the DAG are completed, and search resultsare transmitted to a data destination 1316. These additional phases maybe implemented in a similar manner as described above, and they aretherefore not discussed in detail with respect to searches against acommon storage 1702.

Subsequent to these phases, a set of search results corresponding toeach collection of messages (e.g., as received during a time-window) maybe transmitted to a data destination. On transmission of suchinformation (and potentially verification of arrival of such informationat the data destination), the search head 210 may cause anacknowledgement of each message within the collection to be transmittedto the ingested data buffer 1802. For example, the search head 210 maynotify the query coordinator 1004 that search results for a particularset of information (e.g., information corresponding to a range oftimestamps representing a given time window) have been transmitted to adata destination. The query coordinator 1004 can thereafter notifypartitions used to ingest messages making up the set of information thatthe search results have been transmitted. The partitions can thenacknowledge to the ingested data buffer 300 receipt of the messages. Inaccordance with the pub-sub messaging model, the ingested data buffer1802 may then delete the messages after acknowledgement by subscribingparties. By delaying acknowledgement of messages until after searchresults based on such messages are transmitted to (or acknowledged by) adata destination, resiliency of such search results can be improved orpotentially guaranteed. For example, in the instance that an erroroccurs between receiving a message from the ingested data buffer 1802and search results based on that message being passed to a datadestination (e.g., a worker node 1006 fails, causing a copy of themessage maintained at the worker node 1006 to be lost), the querycoordinator 1004 can detect the failure (e.g., based on heartbeatinformation from a worker node 1006), and cause the worker node 1006 tobe restarted, or a new worker node 1006 to replace the failed workernode 1006. Because the message has not yet been acknowledged to theingested data buffer 1802, the message is expected to still exist withina message queue of the ingested data buffer 1802, and thus, therestarted or new worker node 1006 can retrieve and process the messageas described below. Thus, by delaying acknowledgement of a message,failures of worker nodes 1006 during the process described above can beexpected not to result in data loss within the data intake and querysystem 1001.

In some embodiments, the ingested data buffer 1802 and searchfunctionalities described above may be used to make “enhanced” orannotated data available for searching in a streaming or continuousmanner. For example, search results may in some instances be representedby codes or other machine-readable information, rather than in aneasy-to-comprehend format (e.g., as error codes, rather than textualdescriptions of what such a code represents). Thus, the embodiment ofFIG. 18 may enable a client to define a long-running search that locatescodes within messages of the ingested data buffer 1802 (e.g., viaregular expression or other pattern matching criteria), correlates thecodes to a corresponding textual description (e.g., via a mapping storedin common storage 1702), annotates or modifies the messages to includerelevant textual descriptions for any code appearing within the message,and re-publishes the messages to the ingested data buffer 1802. In thismanner, the information maintained at the ingested data buffer 1802 maybe readily annotated or transformed by searches executed at the system1001. Any number of types of processing or transformation may be appliedto information of the ingested data buffer 1802 to produce searchresults, and any of such search results may be republished to theingested data buffer 1802, such that the search results are themselvesmade available for searching.

As will be appreciated in view of the above description, the use of aningested data buffer 1802 can provide many advantages within the dataintake and query system 1001. Specifically, use of a ingested databuffer 1802 can enable the system 1001 to utilize worker nodes 1006 tosearch not-yet-indexed information, thus decoupling searching of suchinformation from the functionality of data ingestion, as implemented byindexers 206. Moreover, because the ingested data buffer 1802 can makemessages available to both indexers 206 and worker nodes 1006, searchingof not-yet-indexed information by worker nodes 1006 can be expected notto detrimentally effect the operation of the indexers 206. Stillfurther, because the ingested data buffer 1802 can operate according toa pub-sub messaging model, the system 1001 may utilize selectiveacknowledgement of messages (e.g., after indexing by an indexer 206 andafter delivery of search results based on a message to a datadestination) to increase resiliency of the data on the data intake andquery system 1001. Thus, use of an ingested data buffer 1802 cansubstantially improve the speed, efficiency, and reliability ofoperation of the system 1001.

As described in greater detail in greater detail in U.S. patentapplication Ser. No. 15/665,159, entitled “MULTI-LAYER PARTITIONALLOCATION FOR QUERY EXECUTION”, filed on Jul. 31, 2017, and which ishereby incorporated by reference in its entirety for all purposes, thevarious architectures of the system described herein can be used todefine query processing schemes and execute queries based on workloadmonitoring, process and execute queries corresponding to data inexternal data sources, common storage, ingested data buffers,acceleration data stores, indexers, etc., allocate partitions based onidentified dataset sources or dataset destinations, dynamically generatesubqueries for external data sources or indexers, serialize/deserializedata for communication, accelerate query processing using theacceleration data store 1008, etc.

10.0 Combining Datasets

In some cases, a query can indicate that two or more datasets are to becombined in some fashion, such as by using a join or a union operation.Combining datasets can result in significantly larger number of dataentries than the sum of the datasets to be combined.

Accordingly, executing a query on large datasets, in some cases, thesystem can allocate more partitions for combination or expansionoperations than for mapping or reducing operations to avoid partitionshaving too many data entries. For example, in certain cases, the systemcan automatically allocate five, ten, or twenty, times (or more) morepartitions for combination or expansion operations than for mapping orreducing operations. Mapping operations generally operate on a set ofresults or partitions. Reducing operations generally reduce a set ofresults to a smaller set of results, which can result in fewerpartitions being used. Combination or expansion operations generallyincrease a set of results to a larger set of results, which can resultin more partitions being used.

However, while assigning more partitions can result in smallerpartitions overall, some partitions may have a disproportionate numberof the data entries from the datasets. For example, if the data entriesare partitioned based on a particular field value or field-value pairs,one or more field values or field-value pairs may occur significantlymore frequently than others. As such, the partition assigned to storethat field-value pair can end up having a disproportionate number ofdata entries than the other partitions. Similarly, a processor coretasked with processing the data entries of that partition may end uphaving significantly more processing to do as compared to otherprocessor cores. This imbalance can result in a significant delay of theentire set of results until the processor core finishes processing theimbalanced partition.

For example, consider the following search which, includes a joincommand that is to be executed by a distributed system having sixtycores:

“search index=dogfood2007 | fields _time, source | fields − _raw | joinusetime=f left=L right=R where L._time=R._time [search index=dogfood2007| fields _time, sourcetype | fields − _raw] | stats count”

As indicated in the search command, the index dogfood2007 is beingsearched for data entries with fields _time and source. The searchcommand also includes an instruction to join two datasets, L and R,based at least in part on the values in the _time field. Supposing thatthe dogfood2007 index contains approximately 500 million data entriesthe above join returns results in >264 billion data entries.

In some scenarios, the distributed system partitions the 500 milliondata entries based on the field value of each entry that corresponds tothe field that is the subject of the join. For example, one core istasked with processing the data entries that include an identical orsimilar _time field value. However, such a distribution can result inimbalanced partitions in cases where one or more field values are highlyrepetitive and/or have high cardinality.

Consider an instance in which one of the _time field values in the abovesearch has ˜380,000 repetitions. Since the above search involves a selfjoin (joining different datasets that originated from the same datasource or dataset), one of the partitions would contain 380,000 fieldvalue repetitions on both sides (from the L and R datasets) that are tobe joined. The joining of the two sets of 380,000 field values wouldresult in ˜144 billion results in the partition. The processor coreassigned to process the data entries in that partition would be taskedto process the ˜144 billion search, which could result in days of searchexecution time.

Thus, although sixty cores are assigned for the above search, fifty-nineof the cores would no longer be utilized after completing the processingon their relatively smaller partitions, while one core would continue torun for many hours to compute the ˜144 billion result output.

To improve the distribution of the data entries that have a high numberof repetitive field values and high cardinality, the system candetermine and apply a seed value to such data entries such that the dataentries are distributed between multiple partitions, enabling multipleprocessor cores to process them in parallel.

As the additional partitioning can result in additional processingresources, the system can perform an preliminary review to determinewhether to implement the multi-partition operation. This additionalprocessing can be completed by a master node, such as by a search head,query coordinator, or by one or more of the distributed processor coresexecuting the search. Furthermore, in some embodiments, the additionalprocessing can be performed during the query processing stage and/orduring the query execution stage.

In some embodiments, prior to executing the query, the system candetermine whether a multi-partition operation is to be used. Forexample, the system can perform a semantic analysis of the query itselfto determine the likelihood of a significantly imbalanced partition. Thesemantic analysis can include a review of the query command itself. Forexample, if the system determines that the query command does notinclude a combination operation, such as a join operation, the systemcan determine not to implement the multi-partition operation. In somecases, the system can determine not to implement the multi-partitionoperation if the query command indicates a combination based on a fieldthat was previously used in a reduction operation of the query or is asubset of the fields used in a reduction operation.

Further, if the system determines that the query command indicates thatthe combination is based on a field that was not previously used in areduction operation or that the operation just prior to the instructionto combine was a combination or expansion operation, then the system candetermine that the multi-partition operation may be used.

If, following the pre-execution analysis, the system determines that animbalanced partition is possible, then it can monitor the execution ofthe query or instruct the distributed processor cores to monitor theexecution of the query.

During execution of the query, the system can identify the field that isto be used to combine the different datasets, and determine the numberof data entries in each dataset that have the same field value for theidentified field. Using the number of data entries from each datasetthat have the same field value for the field used in the combination,the system can determine whether to implement the multi-partitionoperation.

In some embodiments, if the data entries from each dataset that have thesame field value satisfies a data entries quantity threshold, then thesystem can implement the multi-partition operation. As a non-limitingexample, the system can combine the number of data entries in eachdataset, such as by multiplying the number of data entries that have thesame field value in a first dataset with the number of data entries thathave the same field value in a second dataset. If the product exceeds adata entries quantity threshold, then the system can implement themulti-partition operation.

Following the multi-partition operation, the system can perform asimilar analysis on each of the partitions involved in themulti-partition operation. If the combination of the data entries fromthe datasets in a partition exceeds the data entries quantity thresholdor the memory usage for the sub-partition exceeds a memory levelthreshold, the system can implement the multi-partition operation forthe affected partition. The system can continue to perform themulti-partition operation until the combination of data entries from thedatasets in each partition or sub-partition does not satisfy the dataentries quantity threshold and the memory level threshold.

10.1 Multi-Partition Determination

FIG. 19 is a flow diagram illustrative of an embodiment of a routine1900 implemented by the system to process and execute a query. Oneskilled in the relevant art will appreciate that the elements outlinedfor routine 1900 can be implemented by one or more computingdevices/components that are associated with the system 1000, such as thesearch head 210, search process master 1002, query coordinator 1004and/or worker nodes 1006, or any combination thereof. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 1902, the system receives a query. The system can receive thequery similar in a manner similar to that described above with referenceto block 3802 of FIG. 38

At decision block 1904, the system determines whether the query issusceptible to a significant partition imbalance, such as an imbalancethat could result in a processor core spending significant amounts ofadditional time (non-limiting examples: hours or days) processing thepartition while other processor cores assigned to the same query havecompleted processing their partitions. For example, as part of decisionblock 1904, the system can analyze the syntax or semantics of the queryto determine whether the query is susceptible to a significant partitionimbalance. The system can make this determination in a variety of ways.

In some embodiments, the system can determine whether the query issusceptible to a significant partition imbalance based on whether thequery includes a reduction operation prior to the combination operationand/or whether the datasets are to be combined using a field that is tobe used in a reduction operation prior to the combination operation.Some reduction operations can include, but are not limited to, statscommands, such as, countby, count, etc. or mathematical operations, suchas mean, median, average, etc. Examples of combination operations caninclude, but are not limited to inner joint, outer join (left outer,right outer, full outer), union, etc.

In certain embodiments, such as when no reduction operation has beenperformed on the datasets or the field used in the combination operationdoes not correspond to a field used in a prior reduction operation, thesystem can determine that the query is susceptible to a significantlyimbalanced partition. In certain embodiments, such as when the fieldused in the combination operation corresponds to a field used in a priorreduction operation (e.g., field in the combination is the same field ora subset of the fields used in a prior reduction operation), the systemcan determine that the query is not susceptible to a significantlyimbalanced partition.

As a non-limiting example, if the query indicates that two datasets areto be joined based on the field “_time,” the system can determinewhether the query includes a reduction operation using the field “_time”that is prior to the join. For example, the reduction operation can usethe field “_time” alone or in combination with other fields, such thatthe field “_time” in the join is a subset of the fields used in thereduction operation. In some embodiments, upon determining that thequery includes a reduction operation using the field “_time” prior tothe join, the system can determine that the query is not susceptible toa significantly imbalanced partition. Conversely, in certainembodiments, upon determining that the query does not include anyreduction operations, any reduction operations prior to the join, or areduction operation prior to the join that uses the field “_time,” thesystem can determine that the query is susceptible to a significantlyimbalanced partition.

In some embodiments, the system can determine that the query issusceptible to a significant partition imbalance if one of the datasetsincludes a combination or expansion operation just prior to thecombination operation. In some circumstances, this determination can bemade even if the field in the combination operation matches a field inan earlier reduction operation. For example, if the _time field is usedto reduce two datasets, an expansion operation is performed on one ofthe datasets (with or without the _time field), and the datasets arethen to be combined based on the time field (or any other field in someembodiments), the system can determine that the query is susceptible toa significant partition imbalance.

In certain embodiments, the system can review an inverted index todetermine whether the query is susceptible to a significant partitionimbalance. As described herein, inverted indexes can include informationabout data entries or events that are stored by the system, such as, butnot limited to, relevant fields associated with different data entriesor events, field-value pairs of various data entries or events, a countof the field-value pairs for data entries or events in different datastores or time series buckets, etc.

Accordingly, if the field to be used in the combination operation isincluded in an inverted index, the system can review the invertedindexes associated with the datasets that are to be combined. Forexample, the system can review field-value pairs in the relevantinverted indexes and the quantity of each field-value pair. The systemcan then use the quantity of the field-value pairs to determine whetherthe query is susceptible to a significant partition imbalance. Forexample, if the quantity of a given field-value pair in the invertedindexes associated with the datasets satisfies the data entries quantitythreshold, the system can determine that the query is susceptible to asignificant partition imbalance.

In the event that the system determines that the query is susceptible toa significant partition imbalance, the routine moves to block 1906 andthe system monitors the query during execution to determine a number ofmatching field-value pair data entries in datasets that are to becombined based on the field corresponding to the matching field-valuepair data entries.

The matching field-value pair data entries can correspond to dataentries that have a matching field-value pair (i.e., a combination of afield and field value for that field). It will be understood that eachdataset can include a large number of matching field-value pair dataentries for many different field-value pairs. Furthermore, it will beunderstood that a single data entry can be a matching field-value pairdata entry for different fields and field-value pairs. For example, if adata entry includes the field-value pairs “_time::1:34:00” and“IP_addr::192.168.1.4,” then it can belong to one group of matchingfield-value pair data entries with the field-value pair “_time::1:34:00”and to another group of matching field-value pair data entries with thefield-value pair “IP_addr::192.168.1.4.” Further, although reference ismade herein to a data entry including a field-value pair, in someembodiments, the data entry itself may not expressly identify the field.Rather, the data entry may include a field value that corresponds to afield designated by the system. For example, the data entry may includethe value “192.168.1.4,” which the system identifies as the field valuefor an IP address field.

As part of monitoring the query during execution, the system canidentify the fields that are to be used in a combination operation ofthe datasets. The system can also identify the number of matchingfield-value pairs data entries corresponding to the identified field ineach of the datasets to be combined in the combination operation. Thesystem can determine whether the matching field-value pair data entriesin the different datasets satisfies a data entries quantity threshold.

At decision block 1908, the system determines whether to implement amulti-partition operation for a particular field-value pair. In certainembodiments, the system can determine whether to implement themulti-partition operation based on whether the matching field-value pairdata entries in the datasets satisfy a data entries quantity threshold.In some cases, the system can combine the quantity of matchingfield-value pair data entries from the datasets to determine if the dataentries quantity threshold is satisfied. In certain instances, thesystem can combine the quantity of matching field-value pair dataentries by multiplying or adding the number of matching field-value pairdata entries from each dataset that is to be combined.

In some embodiments, the data entries quantity threshold can be based onthe processing power/speed of the individual processing cores, thenumber of available cores for the query, a timing preference forcompleting the query, etc. For example, the data entries quantitythreshold can be larger for processing cores with more processingpower/speed and smaller for processing cores with less processingpower/speed. In some cases, the data entries quantity threshold can belarger for when fewer cores are used for a particular query or smallerwhen more cores are used for the particular query. In certainembodiments, the data entries quantity threshold can be smaller forqueries that are to be completed in less time than for queries that canbe completed in more time. In certain embodiments, the data entriesquantity threshold can be one million, five, million, ten million ormore data entries.

In certain embodiments, the system can use the inverted indexes, asdescribed above, to determine whether to implement a multi-partitionoperation. Thus the inverted indexes can be used to determine whetherthe query is susceptible to a significantly imbalanced partition and/orwhether to implement a multi-partition operation for a particularfield-value pair.

In the event the system determines to implement the multi-partitionoperation for a particular field-value pair, then the routine moves toblock 1910 and the system implements the multi-partition operation. Someembodiments of the re-partition operation are described in greaterdetail below with reference to FIGS. 20 and 22.

In some embodiments, the multi-partition operation includes partitioningmatching field-value pair data entries from the different datasets intomultiple partitions, such that each partition includes a group or subsetof the combined matching field-value pair data entries from thedifferent datasets (e.g., each partition can include a group of matchingfield-value pair data entries from each of the datasets to be combined).By partitioning matching field-value pair data entries, the system canreduce the size of each partition, as well as the amount of time andprocessing power to process the partition.

In certain cases, as part of the multi-partition operation, the systemcan determine whether to perform a second multi-partition operation onone or more of the partitions formed as a result of the firstmulti-partition operation. In some cases, the system can determine toperform the second multi-partition operation based on the quantity ofdata entries in the particular partition, as described above. Forexample, if the quantity of data entries in the particular partition, ora combination of the data entries, satisfies the data entries quantitythreshold, then the system can perform a multi-partition operation onthat partition, effectively creating sub-partitions or replacementpartitions for that partition.

In some embodiments, the system can also perform a secondmulti-partition operation on one or more of the partitions based on acombined size of the data entries in that partition. For example, if theamount of memory used or required to store the data entries, or the dataentries following a combination operation, in a partition satisfies amemory level threshold, then the system can perform a multi-partitionoperation on that partition. In some cases, the system may have limitedamounts of memory that can be used for each processor core. Accordingly,to avoid exceeding that amount, the system can use a memory levelthreshold.

The memory level threshold can correspond to an acceptable amount ofmemory that the combination of the first subgroup and the second groupcan use. The threshold can vary depending on the number of processorcores in use on a system, the total amount of memory on the device, thetotal amount of available memory, etc. If the amount of memory requiredto store the combination satisfies or exceeds the threshold, the systemcan repeat the multi-partition operation until the combination ofmatching field-value pair data entries in a partition/sub-partitionsatisfy the data entries quantity threshold and the memory levelthreshold. By performing the multi-partition operation at block 1910,the system can avoid a significantly imbalanced partition, therebyreducing the overall runtime of the query.

Following the multi-partition operation, the system continues with thequery execution, such as by combining, or continuing to combine, thedatasets as illustrated in block 1912. In some embodiments the queryexecution can include combining groups of data entries of the datasetsin different partitions that were not part of the multi-partitionoperation or performing the multi-partition operation for otherfield-value pairs.

In addition, as shown in FIG. 19, in the event that the systemdetermines that the query is not susceptible to a significant partitionimbalance or determines not to implement the multi-partition operationfor a given field-value pair or combination operation, the system movesto block 1912 and continues query execution without performing themulti-partition operation for that particular field-value pair orcombination operation, respectively.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 1900. In some cases, one or more blocks can beomitted. For example, decision blocks 1904 and 1908, and thecorresponding no decision paths, can be replaced with a “determine thatquery is susceptible to an imbalance” block and a “determine toimplement multi-partition operation” block, respectively.

As yet another example, for a combination operation, the system cananalyze each field-value pair of the datasets that are to be combined todetermine whether the multi-partition operation is to performed.Further, if the query includes multiple combination operations, thesystem can analyze each combination operation to determine whether toperform the multi-partition operation for relevant data entries of thedatasets. Accordingly, during execution of a query, the multi-partitionoperation may not be performed at all or may be performed one or moretimes for a single query, one or more times for a single combinationoperation (e.g., for different field-value pairs), or one or more timesfor a single field-value pair.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 19 can be implemented in a variety oforders. In some cases, the system can implement some blocks concurrentlyor change the order as desired. For example, the system can continuewith a query execution for some field-value pairs, while concurrentlyexecuting the multi-partition operation for other field-value pairs asdesired. In addition, it will be understood that any of the blocksdescribed herein with reference to routine 1900 can be combined with anyof routines 2000 or 2200, or be combined with or form part of routines1500, 1600. For example, in some cases, the decision block 1904, or asimilar block, can form part processing a query, as described in greaterdetail with reference to block 1504 of FIG. 15. In certain embodiments,the instructions to monitor the query can be generated as part of thequery processing scheme, as described in greater detail with referenceto block 1610 of FIG. 16, and included in the DAG communicated to theworker nodes 1006, as described above.

10.2 Multi-Partition Operation

As described above, in some instances a query includes instructions tocombine multiple datasets based on one or more fields. Each dataset mayinclude multiple field-value pairs that correspond to the one or morefields used to combine the datasets. In some cases, these field-valuepairs can be used to assign matching field-value pair data entries ofthe datasets to different partitions. For example, matching field-valuepair data entries from the different datasets can be assigned to thesame partition. However, as there may exist a large number of matchingfield-value-pair data entries assigned to the same partition, the systemcan determine that at least some of the matching field-value pair dataentries should be further partitioned. In such cases, the system canallocate the data that was to be assigned to the single partition tomultiple partitions.

Accordingly, FIG. 20 is a flow diagram illustrative of an embodiment ofa multi-partition routine 2000 implemented by the system on matchingfield-value pair data entries. In some embodiments, the multi-partitionroutine 2000 can correspond to the multi-partition operation referencedin block 1910 of FIG. 19. One skilled in the relevant art willappreciate that the elements outlined for routine 2000 can beimplemented by one or more computing devices/components that areassociated with the 1000, such as the search head 210, search processmaster 1002, query coordinator 1004 and/or worker nodes 1006, or anycombination thereof. Thus, the following illustrative embodiment shouldnot be construed as limiting.

At block 2002, the system identifies a first group of data entries froma first dataset and a second group of data entries from a seconddataset. The first group of data entries can correspond to data entriesof the first dataset that have a field-value pair that corresponds to afield that is being used to combine the first dataset with a seconddataset. Similarly, the second group of data entries can correspond todata entries of the second dataset that have a field-value pair thatmatches the field-value pair of the data entries of the first group.

As mentioned above, the datasets that are to be combined can correspondto the same original data source or dataset that may have been processeddifferently or can correspond to different original data sources ordatasets.

At block 2004, the system assigns data entries of the first group to aplurality of partitions. The allocated partitions can correspond topartitions that are being used to process other matching field-valuepair data entries or other data entries of the datasets, or they cancorrespond to separate partitions that are used to process just thesubgroups of the first group.

In some embodiments, the system assigns each data entry of the firstgroup to one of the allocated partitions. In certain embodiments, thesystem assigns the data entries of the first group to a partition in arandom or pseudo-random fashion. By randomly assigning the data entriesto the different partitions, the system can reduce overhead as comparedto sequentially assigning the data entries to the different partitions.However, it will be understood that the system can sequentially assignthe data entries of the first group to the different partitions, or useother mechanisms to assign the data entries of the first group to thedifferent partitions as desired. Once the system assigns the dataentries of the first group to the plurality of partitions, eachpartition can include a first subgroup of data entries that correspondto a subset of the first group.

In some embodiments, the system can calculate a seed value, and use theseed value to partition the data entries of the first group. In someembodiments, the seed value can be determined based on the first groupof data entries and the second group of data entries from the seconddataset. In certain cases, the system can calculate the seed value basedon the number of data entries in the first group of data entries and thenumber of data entries in the second group of data entries. Furthermore,in the event more than two datasets are to be combined, the system canuse the number of data entries in the additional datasets to determinethe seed value.

In certain embodiments, to calculate the seed value, the system uses adata entries quantity threshold. For example, the seed value can bedetermined by dividing the number of data entries after combining thenumber of data entries in the first group and the number of data entriesin the second group by the data entries quantity threshold. In certainembodiments, following the division, the system can round up thequotient to determine the seed value. In some embodiments, the seedvalue can be calculated as:

${{seed}\mspace{14mu}{value}} = {{ceiling}\mspace{14mu}{of}\sqrt{\frac{{entries}\mspace{14mu}{in}\mspace{14mu}{group}\mspace{14mu} 1*{entries}\mspace{14mu}{in}\mspace{14mu}{group}\mspace{14mu} 2}{{data}\mspace{14mu}{entries}\mspace{14mu}{quantity}\mspace{14mu}{threshold}}}}$

However, it will be understood that the seed value can be determined ina number of ways to reduce the number of entries in a partition so as tonot satisfy the data entries quantity threshold.

In some cases, the system can use the seed value to allocate partitionsand to assign the data entries of the first group to the differentpartitions. For example, the system can allocate a number of partitionsequal to the seed value. In addition, the system can randomly orsequentially assign the data entries of the first group to the differentpartitions using the seed value, for example, by modulating a randomlygenerated number by the seed value and using the result to assign aparticular data entry to a partition.

At block 2006, for at least one partition, the system combines the dataentries of the first subgroup of the first group with the data entriesof the second group. In certain embodiments, the system performs block2006 for all partitions. The system can access the entries of the secondgroup for combination with the data entries of the subgroup of the firstgroup in a variety of ways. In some embodiments, the system usesmultiple processors or nodes to combine the data entries in thedifferent partitions. In certain embodiments, a distinct processor isused to combine the data entries for each partition.

In some embodiments, a copy of each data entry of the second group canbe assigned to each partition. For example, the system can make a copyof each entry of the second group and assign it to each allocatedpartition. In certain embodiments, each core processing a partition canread a memory location that stores the data entries of the second group.The system can use the data read from the relevant memory location tocombine the first subgroup with the second subgroup.

In certain embodiments, the system can assign the second group to theallocated partitions, similar to the manner in which the data entries ofthe first group are assigned, such that each partition includes a secondsubgroup of data entries that correspond to the second group. For eachpartition, the system can generate a copy of each data entry of thesecond subgroup, reassign the copies (or the original) to the otherpartitions, and reform the second subgroup to include the data entriesassigned from other partitions. The system can then combine the reformedsecond subgroup with the first subgroup.

The combination of the first subgroup with the second group can resultin a larger number of data entries than the sum of the first subgroupand second group. In some cases, the resultant number of data entriescan correspond to the product of the number of entries in the firstsubgroup and the number of entries in the second group. In certainembodiments, the system can combine the first subgroup with the secondgroup based on the matching field value. For example, each data entry inthe combined subgroup can correspond to a unique combination of a dataentry of the first subgroup and a data entry of the second group.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 2000. In addition, it will be understood that anyof the blocks described herein with reference to routine 2000 can becombined with any of routines 1900 or 2200. In some cases, one or moreblocks can be omitted or repeated. For example, the system can determinethat the second group is smaller than the first group and assign thefirst group to the different partitions based on that determination. Asanother example, the system can perform additional operations on thedata entries of the combined subgroup or the combined first and seconddataset. As described herein, the instruction to combine the datasetscan be part of a combination operation that is only one operation of aquery. Accordingly, following the combination operation, the system canperform additional operations on the data entries that resulted from thecombination operation.

As yet another example, in addition to combining the subgroups andgroups described above, the routine 2000 can also combine other groupsof the datasets and/or perform other tasks to complete the execution ofthe query. As described above, in some cases, the system does notperform a multi-partition operation for all data entries. Thus, routine2000 can further include the system combining the first and seconddatasets, with blocks 2002, 2004, and 2006 being performed on a subsetof the data entries of the datasets.

As a non-limiting example, the system can partition datasets usingmatching field-value pairs that correspond to a field that is used tocombine the datasets. Further, the system can perform the routine 2000on a subset of the partitions, such as the partitions that includematching field-value pair data entries that, when combined, satisfy thedata entries quantity threshold. In some cases, the system only performsthe routine 2000 on the partitions that include matching field-valuepair data entries that, when combined, satisfy the data entries quantitythreshold.

As described above, the routine 2000 can be repeated multiple times fora particular field-value pair (e.g., in the event the combination ofevents of the subgroup of the first group and the second group satisfiesa data entries quantity threshold) or for a particular combinationoperation (e.g., for multiple field-value pairs in the datasets to becombined). In addition, if the query includes multiple combinationoperations, the system can repeat the routine 2000 for one or morefield-value pairs in each combination operation.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 20 can be implemented in a variety oforders. In some cases, the system can concurrently assign data entriesof the first group to the different partitions, while concurrentlycombining the data entries of the subgroup of the first group with thedata entries of the second group. In addition, the system canconcurrently implement the routine 2000 for multiple field-value pairsas part of a combination operation.

Similarly, the system can implement the routine 2000 for one or morefield-value pairs, while concurrently combining other field-value pairswithout routine 2000. In some embodiments, when combining multipledatasets as part of a combination operation, the system can determinethat data entries assigned to one partition are to be assigned tomultiple partitions, while data entries assigned to a second partitionare not to be assigned to multiple partitions. For example, the dataentries assigned to the second partition may not satisfy the dataentries quantity threshold. As such, the multi-partition operation maynot be used for that partition. However, the system can combine the dataentries in the second partition, while concurrently using the routine2000 to combine the data entries of the first partition.

FIG. 21 is a diagram illustrating an embodiment of a join operationperformed on two datasets. It will be understood that the although thedatasets in the illustrated example are relatively small, the datasetsused by the system can be significantly larger and include millions oreven billions of data entries. Accordingly, the illustrated exampleshould be not construed as limiting.

In the illustrated embodiment, Dataset 1 and Dataset 2, illustrated at2102, are to be joined based on the field time. For purposes of thisexample, the data entries quantity threshold is five. In addition, inthe illustrated example, the data entries of Dataset 1 include fieldvalues for the fields time and source and the data entries in Dataset 2include field values for the fields time and source type. It will beunderstood that the illustrated data entries are examples only andshould not be construed as limiting.

As described in greater detail above with reference to block 1906 ofFIG. 19, the system can monitor the number of field-value pairs in eachdataset that correspond to the field being used in the join operation.As part of the monitoring, the system can determine whether the matchingfield-value pair data entries in the different datasets satisfy the dataentries quantity threshold. When the system analyzes the field-valuepair time::1, it determines that the combination of the matchingfield-value pair entries for Dataset 1 and Dataset 2 is six, whichsatisfies the data entries quantity threshold of five. Accordingly, thesystem can proceed to implement a multi-partition operation on the dataentries with a field-value pair of time::1.

In this example, the system determines a seed value of two based on thequantity of matching field-value pair data entries in each dataset andthe data entries quantity threshold. Using the seed value, the systemrandomly assigns a seed to each matching field-value pair data entry inDataset 2 as illustrated by the seeded Dataset 2 2104. In some cases,Dataset 2 can be selected for seeding based on a determination that theDataset 1 has fewer matching field-value pair data entries than Dataset2. However, it will be understood that Dataset 2 can be selected forseeding in a variety of ways, such as randomly, because it has morematching field-value pair data entries than Dataset 1, etc.

Using the seeding, the system allocates the matching field-value pairdata entries of Dataset 2 to Partition 1 and Partition 2 as illustratedat 2106. In some cases, the number of partitions used corresponds to theseed value. For example, as the seed value is two in this example, thesystem uses two partitions and allocates the matching field-value pairdata entries of Dataset 2 based on the number of partitions. Inaddition, as illustrated, in some embodiments, the partitions maintain aseparation between the data from Dataset 1 and Dataset 2, or otherwiseidentify the matching field-value pair data entries based on the datasetfrom which they came.

As illustrated at 2108, the system makes the matching field-value pairdata entries of Dataset 1 available to each of the partitions. In somecases, this can be done by copying the matching field-value pair dataentries of Dataset 1 to each partition, enabling the processor coresthat process the different partitions to access the matching field-valuepair data entries in a read-only fashion, or partitioning, duplicating,and repartitioning the matching field-value pair data entries of Dataset2 as described in greater detail below with reference to FIG. 22.

As illustrated at 2110, in each partition, the system joins the matchingfield-value pair data entries from the different datasets. Although notillustrated in this example, it will be understood that the join of thematching field-value pair data entries in Partition 1 and Partition 2can occur before, after, or concurrently with each other and/or with thejoin performed on the other data entries of the datasets. For example,in addition to Partition 1 and Partition 2, used to join the matchingfield-value pair data entries for time 1, an additional one or morepartitions can be concurrently used to join the matching field-valuepair data entries for time 2, 3, and 4. As illustrated, given that thecombination of matching field-value pair data entries for time 4satisfies the data entries quantity threshold, the system can generatemultiple partitions to process the matching field-value pair dataentries for time 4. One example of partitioning the matching field-valuepair data entries for time 4 is described below with reference to FIG.23.

In some embodiments, the seeds used to assign the different data entriesto the different partitions (e.g., 0.1 and 0.2) can remain with the dataentries. In this way the system can track the different subgroups of thefirst group. In certain embodiments, the seeds can be removed followingthe combination operation, and/or as part of or after a subsequentoperation. In some cases, the seeds can remain until a reductionoperation is performed using the data entries.

FIG. 22 is a flow diagram illustrative of an embodiment of amulti-partition routine 2200 implemented by the system to partitionmatching field-value pair data entries. One skilled in the relevant artwill appreciate that the elements outlined for routine 2200 can beimplemented by one or more computing devices/components that areassociated with the 1000, such as the search head 210, search processmaster 1002, query coordinator 1004 and/or worker nodes 1006, or anycombination thereof. Thus, the following illustrative embodiment shouldnot be construed as limiting.

At block 2202, the system identifies a first group and a second groupassociated with the multi-partition operation. As mentioned, the firstgroup of data entries can correspond to data entries of the firstdataset that have a field-value pair that corresponds to a field that isbeing used to combine the first dataset with a second dataset.Similarly, the second group of data entries can correspond to dataentries of the second dataset that have a field-value pair that matchesthe field-value pair of the data entries of the first group.

At block 2204, the system identifies the first group as the apartitioning group. In some embodiments, the system identifies the firstgroup as the partitioning group based on a determination that the secondgroup of data entries has fewer data entries than the first group ofdata entries. However, it will be understood that the system canidentify the partition group in a variety of ways. In some cases, thesystem can identify the first group as the partitioning group based on adetermination that it is the same size as or larger than the secondgroup or based on a default setting. In some embodiments, the systemdetermines the quantity of the first group and the second group using astats command or other command that provides a count of the number ofdata entries in the first group and the second group. In someembodiments, the command can be executed as a background process andwithout the knowledge of the user. Using the data, the system candetermine that the second group has fewer data entries then the firstgroup.

At block 2206, the system assigns each data entry of the first group andeach data entry of the second group to one of a plurality of partitions.The assignment of the data entries from the different groups can beaccomplished similar to the manner described above. For example, thesystem can calculate a seed value as described in greater detail above.Further, the system can use the seed value to allocate partitions and/orassign the data entries of the first and second groups to thepartitions.

Once the first and second groups have been partitioned and thepartitions include the first and second subgroups of the first andsecond groups, the system can perform blocks 2208, 2210, 2212, and 2214for at least one partition. However, in certain embodiments, the systemperforms blocks 2208, 2210, 2212, and 2214 on each partition. Inaddition, in some embodiments, the system can use one or more processorsto perform blocks 2208, 2210, 2212, and 2214 on the differentpartitions. In some cases, a distinct processor can be assigned toperform blocks 2208, 2210, 2212, and 2214 on each partition.

At block 2208, the system duplicates the second subgroup. In certaincases, the system duplicates the second subgroup based on theidentification of the first group as the partitioning group. In somecases, the system can duplicate each data entry of the second subgroupbased on the number of partitions that hold a subgroup of the secondgroup. For example, if seven partitions hold a subgroup of the secondgroup, the system can generate six duplicates for each data entry of thesecond subgroup such that a total of seven identical data entries exist.

At block 2210, the system reassigns the data entries of the secondsubgroup, or assigns duplicates of the second subgroup, to the otherpartitions. In some cases the system reassigns the data entries so thateach partition includes the data entries corresponding to the secondgroup. In some cases, as the system generates the duplicates for eachdata entry it can also assign it to a partition. In certain embodiments,the duplicates can be assigned in a sequential manner such that forpartition 1, the first duplicate of a data entry is assigned topartition 2, the second duplicate of a data entry is assigned topartition 3, and so on. However, it will be understood that the dataentries can be assigned in any manner as desired. For example, in somecases, all of the original data entries of the second group can beassigned to one partition, all of the first duplicates in each partitioncan be assigned to a second partition, and so on.

At block 2212, the system reforms the second subgroup to include one ormore data entries assigned to it from other partitions. As the systemre-assigns data entries, assigns duplicate data entries, or repartitionsthe second group of data entries so that each partition includes a setof the second group, the second subgroup in each partition can bereformed to include the data entries assigned from other partitions.Further, once the repartitioning is complete, each partition can includea complete set of the second group. Accordingly, in some embodiments,the reformed second subgroup can correspond to, or be the same as, thesecond group of data entries.

At block 2214, the system generates a combined subgroup based the firstsubgroup and the reformed second group. As described above, the systemcan combine the first subgroup and the reformed second group in avariety of ways as desired, or depending on the combination operation tobe performed by based on the query. In some cases, the number of dataentries in the combined subgroup can correspond to the product of thenumber of data entries in the first subgroup and the number of entriesin the reformed second subgroup or second group. In certain embodiments,the system can combine the first subgroup with the second group based onthe matching field value such that data entries in the combined subgroupincludes the field value, at least one value from a data entry in thefirst subgroup, and at least one value from a data entry in the secondgroup or reformed second subgroup. Furthermore, the system can generatethe combined subgroup by generating a data entry for each uniquecombination of a data entry in the first group with a data entry in thesecond group or reformed second subgroup.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 2200. In some cases, the routine can includeadditional blocks for performing additional functions on the partitionsthat include the combined subgroup. For example, following thecombination operation that generates the different operations, the nodecan perform a reduction operation that results in fewer data entriesand/or reduces the number of partitions used to hold the data entries.Similarly, the node can perform an expansion operation that results inmore data entries and/or increases the number of partitions used to holdthe data entries. In certain cases, the system can retain the seedvalues assigned to the different data entries following the combinationoperation, or discard the seed value as part of a subsequent operation.In certain embodiments, the system can discard the seed value assignedto the different data entries, as part of or after a reductionoperation.

In some cases, one or more blocks can be omitted or repeated. Forexample, blocks 2208, 2210, or 2212 can be combined into a single block.In addition, the system can also combine data entries of partitions thatwere not subject to the seeding or repartitioning described above. Thecombination of data entries of partitions that were not subject to theseeding or repartitioning can be done before, after, or concurrentlywith the blocks of routine 2200. In addition, it will be understood thatany of the blocks described herein with reference to routine 2200 can becombined with any of routines 1900 or 2000.

As described above, the routine 2200 can be repeated multiple times fora particular field-value pair (e.g., in the event the combination ofevents of the subgroup of the first group and the second group satisfiesa data entries quantity threshold) or for a particular combinationoperation (e.g., for multiple field-value pairs in the datasets to becombined). In addition, if the query includes multiple combinationoperations, the system can repeat the routine 2200 for one or morefield-value pairs in each combination operation.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 22 can be implemented in a variety oforders. In some cases, the system can concurrently duplicate the secondgroup, assign duplicate entries to other partitions, and reform thesecond subgroup.

FIG. 23 is a diagram illustrating an embodiment of a join operation ofDataset 1 and Dataset 2 described above with reference to FIG. 3 for thefield-value pair time::4. As discussed, Dataset 1 and Dataset 2 are tobe joined based on the field time with a data entries quantity thresholdof five. When the system analyzes the field-value pairs for time 4, itdetermines that the combination of the matching field-value pair entriesfor Dataset 1 and Dataset 2 is twelve, which satisfies the data entriesquantity threshold of five. Accordingly, the system can proceed toimplement a multi-partition operation on the data entries with afield-value pair of time::4.

Based on the quantity of matching field-value pair data entries for time4 in Datasets 1 and 2 and the data entries quantity threshold, thesystem determines a seed value of three.

In this example, using the seed value, the system randomly assigns eachmatching field-value pair data entry in Dataset 1 and Dataset 2 to apartition as illustrated at 2304, and allocates the matching field-valuepair data entries of Dataset 1 and Dataset 2 to Partition 1, Partition2, or Partition 3 based on the assignment, as illustrated at 2306. Asdiscussed above, the number of partitions can correspond to the seedvalue. Further, as illustrated, in some embodiments, the partitions canretain information indicating the dataset from which each matchingfield-value pair data entry came.

As shown at 2308, the system duplicates the matching field-value pairdata entries from Dataset 1 in each partition. In some cases, Dataset 1can be selected for duplication based on a determination that theDataset 1 has fewer matching field-value pair data entries than Dataset2. However, it will be understood that Dataset 1 can be selected forduplication in a variety of ways, such as by random selection, etc.

In the illustrated embodiment, the system also seeds the duplicatematching field-value pair data entries, or duplicate data entries, forassignment to the other partitions. In some cases, the system cansequentially seed the duplicate data entries for assignment to the otherpartitions. However, it will be understood that the system can allocatethe matching field-value pair data entries that correspond to Dataset 1for assignment to the different partitions in a variety of ways asdiscussed above.

As shown at 2310, the system repartitions the matching field-value pairdata entries that correspond to Dataset 1 so that each partitionincludes matching field-value pair data entries that correspond toDataset 1. As described above, this can be done by repartitioningduplicate data entries from each partition to another partition orotherwise reassigning the matching field-value pair data entries thatcorrespond to Dataset 1 to the different partitions.

In addition, as shown at 2310, the system determines that the number ofmatching field-value pair data entries in Partition 2 satisfies the dataentries quantity threshold. Accordingly, the system determines a seedvalue (2), and assigns the matching field-value pair data entries inPartition 2 to one of two partitions, Partition 2.1 and Partition 2.2,as illustrated at 2312.

As shown at 2314, the system allocates the matching field-value pairdata entries in Partition 2 to the Partitions 2.1 and 2.2, and based ona determination that the number of matching field-value pair dataentries in the Dataset 2 portion of Partition 2 is less than the numberof matching field-value pair data entries in the Dataset 1 portion ofPartition 2, the system duplicates the matching field-value pair dataentries in the Dataset 2 portion of Partitions 2.1 and 2.2.

As shown at 2316, the system reallocates the matching field-value pairdata entries that correspond to the Dataset 2 portion of Partition 2such that Partitions 2.1 and 2.2 each include the matching field-valuepair data entries that correspond to the Dataset 2 portion of Partition2.

As illustrated at 2318, in each of the Partitions 1, 2.1, 2.2, and 3,the system joins the matching field-value pair data entries from thedifferent datasets. Although not illustrated in this example, it will beunderstood that the join of the matching field-value pair data entriesin the Partitions 1, 2.1, 2.2, and 3, can occur before, after, orconcurrently with each other and/or with the join performed on the otherdata entries of the datasets. For example, as discussed above withreference to FIG. 3, one or more partitions can be concurrently used tojoin the matching field-value pair data entries for time 1, 2, and 3.

Although FIGS. 19-23 have been described with reference to the system1000, it will be understood that the concepts described herein can beused in any distributed data processing system where datasets are to becombined in some fashion.

11.0. Hardware Embodiment

FIG. 24 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented. For example, the hardware architecture of a computingsystem 72 can be used to implement any one or more of the functionalcomponents described herein (e.g., indexer, data intake and querysystem, search head, data store, server computer system, edge device,etc.). In some embodiments, one or multiple instances of the computingsystem 72 can be used to implement the techniques described herein,where multiple such instances can be coupled to each other via one ormore networks.

The illustrated computing system 72 includes one or more processingdevices 74, one or more memory devices 76, one or more communicationdevices 78, one or more input/output (I/O) devices 80, and one or moremass storage devices 82, all coupled to each other through aninterconnect 84. The interconnect 84 may be or include one or moreconductive traces, buses, point-to-point connections, controllers,adapters, and/or other conventional connection devices. Each of theprocessing devices 74 controls, at least in part, the overall operationof the processing of the computing system 72 and can be or include, forexample, one or more general-purpose programmable microprocessors,digital signal processors (DSPs), mobile application processors,microcontrollers, application-specific integrated circuits (ASICs),programmable gate arrays (PGAs), or the like, or a combination of suchdevices.

Each of the memory devices 76 can be or include one or more physicalstorage devices, which may be in the form of random access memory (RAM),read-only memory (ROM) (which may be erasable and programmable), flashmemory, miniature hard disk drive, or other suitable type of storagedevice, or a combination of such devices. Each mass storage device 82can be or include one or more hard drives, digital versatile disks(DVDs), flash memories, or the like. Each memory device 76 and/or massstorage device 82 can store (individually or collectively) data andinstructions that configure the processing device(s) 74 to executeoperations to implement the techniques described above.

Each communication device 78 may be or include, for example, an Ethernetadapter, cable modem, Wi-Fi adapter, cellular transceiver, basebandprocessor, Bluetooth or Bluetooth Low Energy (BLE) transceiver, or thelike, or a combination thereof. Depending on the specific nature andpurpose of the processing devices 74, each I/O device 80 can be orinclude a device such as a display (which may be a touch screendisplay), audio speaker, keyboard, mouse or other pointing device,microphone, camera, etc. Note, however, that such I/O devices 80 may beunnecessary if the processing device 74 is embodied solely as a servercomputer.

In the case of a client device (e.g., edge device), the communicationdevices(s) 78 can be or include, for example, a cellulartelecommunications transceiver (e.g., 3G, LTE/4G, 5G), Wi-Fitransceiver, baseband processor, Bluetooth or BLE transceiver, or thelike, or a combination thereof. In the case of a server, thecommunication device(s) 78 can be or include, for example, any of theaforementioned types of communication devices, a wired Ethernet adapter,cable modem, DSL modem, or the like, or a combination of such devices.

A software program or algorithm, when referred to as “implemented in acomputer-readable storage medium,” includes computer-readableinstructions stored in a memory device (e.g., memory device(s) 76). Aprocessor (e.g., processing device(s) 74) is “configured to execute asoftware program” when at least one value associated with the softwareprogram is stored in a register that is readable by the processor. Insome embodiments, routines executed to implement the disclosedtechniques may be implemented as part of OS software (e.g., MICROSOFTWINDOWS® and LINUX®) or a specific software application, algorithmcomponent, program, object, module, or sequence of instructions referredto as “computer programs.”

12.0 Terminology

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor (e.g., processingdevice(s) 74), will cause a computing device to execute functionsinvolving the disclosed techniques. In some embodiments, a carriercontaining the aforementioned computer program product is provided. Thecarrier is one of an electronic signal, an optical signal, a radiosignal, or a non-transitory computer-readable storage medium (e.g., thememory device(s) 76).

Any or all of the features and functions described above can be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to convey that an item, term, etc. may beeither X, Y or Z, or any combination thereof. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of X, at least one of Y and at least one of Z toeach be present. Further, use of the phrase “at least one of X, Y or Z”as used in general is to convey that an item, term, etc. may be eitherX, Y or Z, or any combination thereof.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. Two or more components of a system can be combinedinto fewer components. Various components of the illustrated systems canbe implemented in one or more virtual machines, rather than in dedicatedcomputer hardware systems and/or computing devices. Likewise, the datarepositories shown can represent physical and/or logical data storage,including, e.g., storage area networks or other distributed storagesystems. Moreover, in some embodiments the connections between thecomponents shown represent possible paths of data flow, rather thanactual connections between hardware. While some examples of possibleconnections are shown, any of the subset of the components shown cancommunicate with any other subset of components in variousimplementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes can be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C. sec. 112(f) (AIA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U. S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

What is claimed:
 1. A method, comprising: based at least in part onreceipt of a search query, identifying a field-value pair included in aplurality of data sets, the field-value pair corresponding to aplurality of data entries in the plurality of data sets; determining,based at least in part on an inverted index corresponding to thefield-value pair, that the plurality of data entries satisfy a dataentries threshold; and performing, based on the determining that theplurality of data entries satisfy the data entries threshold, amulti-partition operation on the plurality of data entries.
 2. Themethod of claim 1, the method further comprising: based at least in parton determining that the plurality of data entries satisfy the dataentries threshold, determining that the search query is susceptible togenerating a first partition that includes more data entries than asecond partition.
 3. The method of claim 1, the method furthercomprising: based at least in part on determining that the plurality ofdata entries satisfy the data entries threshold, monitoring execution ofthe search query, wherein performing the multi-partition operation isfurther based on the monitoring execution of the search query.
 4. Themethod of claim 1, wherein the identifying the field-value pair is basedat least in part on syntax of the search query.
 5. The method of claim1, wherein the search query is associated with a first data set and asecond data set of the plurality of data sets.
 6. The method of claim 1,wherein the search query is associated with a first data set and asecond data set of the plurality of data sets, wherein the first dataset comprises a first set of the plurality of data entries and thesecond data set comprises a second set of the plurality of data entries,wherein the method further comprises: assigning the first set of theplurality of data entries to a plurality of partitions based on thefield-value pair; and assigning the second set of the plurality of dataentries to the plurality of partitions based on the field-value pair. 7.The method of claim 1, wherein the search query is associated with afirst data set and a second data set of the plurality of data sets,wherein the first data set corresponds to a first data set source andthe second data set corresponds to a second data set source.
 8. Themethod of claim 1, wherein the search query is associated with a firstdata set and a second data set of the plurality of data sets, whereinthe first data set and the second data set correspond to a same data setsource.
 9. The method of claim 1, further comprising: parsing the searchquery upon receipt of the search query; wherein the performing themulti-partition operation is further based at least in part on theparsing the search query.
 10. The method of claim 1, further comprising:identifying expansion operations to be executed as part of the searchquery; wherein the performing the multi-partition operation is furtherbased at least in part on the identifying the expansion operations. 11.The method of claim 1, further comprising: determining that an expansionoperation of the search query is to be performed prior to a combinationoperation of the search query; wherein the performing themulti-partition operation is further based at least in part on thedetermining that the expansion operation is to be performed prior to thecombination operation.
 12. The method of claim 1, further comprising:determining that no reduction operation is to be performed prior to acombination operation of the search query; wherein the performing themulti-partition operation is further based at least in part on thedetermining that no reduction operation is to be performed prior to thecombination operation.
 13. The method of claim 1, further comprising:determining that a field to be used in a combination operation of thesearch query is different from a field to be used in a reductionoperation of the search query prior to the combination operation;wherein the performing the multi-partition operation is further based atleast in part on the determining that the field to be used in thecombination operation is different from the field to be used in thereduction operation.
 14. The method of claim 1, wherein the field-valuepair is associated with a field to be used in a combination operation.15. The method of claim 1, wherein determining, based at least in parton the inverted index corresponding to the field-value pair, that theplurality of data entries satisfy the data entries threshold comprises:identifying a field of the field-value pair; and identifying a quantityof the field-value pair from one or more field-value pair entries of aplurality of field-value pair entries in a first inverted index, whereinthe first inverted index is associated with the plurality of data sets,and wherein the one or more field-value pair entries include a matchingfield-value pair for the field-value pair.
 16. The method of claim 1,wherein the multi-partition operation comprises: allocating a firstgroup of the plurality of data entries to a plurality of partitions suchthat each of the plurality of partitions includes a subgroup of dataentries of the first group of the plurality of data entries; and foreach partition, combining values of the data entries of the subgroupwith values of the data entries of a second group of the plurality ofdata entries.
 17. A computing system, comprising: memory; and one ormore processing devices coupled to the memory and configured to: basedat least in part on receipt of a search query, identify a field-valuepair included in a plurality of data sets, the field-value paircorresponding to a plurality of data entries in the plurality of datasets; determine, based at least in part on an inverted indexcorresponding to the field-value pair, that the plurality of dataentries satisfy a data entries threshold; and perform, based on theplurality of data entries satisfying the data entries threshold, amulti-partition operation on the plurality of data entries.
 18. Thesystem of claim 17, wherein the one or more processing devices arefurther configured to: based at least in part on determining that theplurality of data entries satisfy the data entries threshold, determinethat the search query is susceptible to generating a first partitionthat includes more data entries than a second partition. 19.Non-transitory computer readable media comprising computer-executableinstructions that, when executed by a computing system, cause thecomputing system to: based at least in part on receipt of a searchquery, identify a field-value pair included in a plurality of data sets,the field-value pair corresponding to a plurality of data entries in theplurality of data sets; determine, based at least in part on an invertedindex corresponding to the field-value pair, that the plurality of dataentries satisfy a data entries threshold; and perform, based on theplurality of data entries satisfying the data entries threshold, amulti-partition operation on the plurality of data entries.
 20. Thenon-transitory computer readable media of claim 19, wherein thecomputer-executable instructions further cause the computing system to:based at least in part on determining that the plurality of data entriessatisfy the data entries threshold, determine that the search query issusceptible to generating a first partition that includes more dataentries than a second partition.